P-1 Extracting Multi-Dimensional Signal Features for Content-Based Visual Query ABSTRACT Future large visual information systems -LRB- such as image databases and video servers -RRB- require effective and efficient methods for indexing , accessing , and manipulating images based on visual content . This paper focuses on automatic extraction of low-level visual features such as texture , color , and shape . Continuing our prior work in compressed video manipulation , we also propose to explore the possibility of deriving visual features directly from the compressed domain , such as the DCT and wavelet transform domain . By stressing at the low-level features , we hope to achieve generic techniques applicable to general applications . By exploring the compressed-domain content extractability , we hope to reduce the computational complexity . We also propose a quad-tree based data structure to bind various signal features . Integrated feature maps are proposed to improve the overall effectiveness of the feature-based image query system . Current technical progress and system prototypes are also described . Part of the prototype work has been integrated into the Multimedia/VOD testbed in the Advanced Image Lab of Columbia University . 1 . Introduction As massive amounts of images and video are being produced everyday at a rapid speed , the challenging task of designing advanced Visual Information Systems -LRB- VIS -RRB- to manage and process these visual data is required . Effective extraction of visual features or contents is needed to provide meaningful index of and access to visual data . Most existing approaches to image indexing and retrieval use the textual keyword -LSB- 4,5 -RSB- . Search and retrieval are performed on the keyword records and the associated images are retrieved after the textual search is complete . Some image databases provide enhancement by supporting query by pictorial examples for constrained applications , such as geographic satellite images , mechanic design diagrams , and human facial images -LSB- 6,11,28 -RSB- . Enhancement can be achieved by using the so-called ? iconic indexing ? concept to explore the spatial relationships among image objects -LSB- 12 -RSB- . Also mentioned in the literature is the semantic level descriptions , such as ? person A in front of a stone house ? or ? a high-speed racing car ? . Contents at this level usually require user input but usually are not complete nor consistent -LRB- e.g. , different users may have different interpretations -RRB- . Also , the vocabulary used in describing image contents is usually domain-dependent . This study takes a different approach based on image processing and computer vision technologies . We will explore generic low-level visual features for applications of content-based visual query . Instead of asking users to specify image content by text , we would like to support image access systems which allow users to search through visual databases by using fundamental feature sets derived from object texture , color , shape , and motion , etc. . These visual features are at a relatively low level , compared to the semantic content mentioned above . Although closer to human subjective perceptions , high-level visual semantic content is still very difficult for computers to extract without user assistance . By starting from the low-level visual features , we hope these features can be more generic and less constrained for potential applications . More importantly , automatic extraction algorithms of these visual features will remain feasible . Automatic mechanisms without substantial user involvement are essential for many image database and storage applications , such as satellite picture databases and large multimedia digital libraries . The concept of Query-by-Image-Content -LRB- QBIC -RRB- has been successfully and comprehensively demonstrated in -LSB- 24 -RSB- . However , user Shih-Fu Chang and John R. Smith Department of Electrical Engineering & Center for Telecommunications Research Columbia University , New York , NY 10027 , U.S.A. SPIE Symposium on Visual Communications and Signal Processing , May 1995 P-2 manual input was used in extracting image content . Also , synergistic relations between feature extraction and image compression were not explored either . Another critical technological requirement for VIS is image compression . Traditionally , image compression has focused solely on the compression performance factors , such as compression rate and computational complexity . In the context of VIS design , we believe considerations of image compression and feature extraction should be integrated together . This actually fits closely the recent trend of image coding effort in the research community -LSB- 29 -RSB- . In this paper , we will describe techniques for extracting visual signal features directly in the compressed domain and binding various features in a unified representation platform . This compressed-domain concept has been applied in image manipulations and filtering for different video applications -LSB- 1 -RSB- . Figure 1 illustrates the concept of compresseddomain image technology . Our focus on low-level signal features does not exclude the usefulness of existing content description and indexing mechanisms such as keywords and user-provided semantics . Instead , we believe that a great multiplicity of query/indexing mechanisms including high-level semantics , and low-level visual features should be provided to accommodate the challenging needs of image indexing and access . We first discuss various feature extraction techniques for texture , color , and shape . We describe a quadtree representation for binding these visual features . An integrated feature map is then proposed to integrate different visual features to improve the usefulness of feature-based image query . For texture and shape , we also explore the capability of extracting features from the compressed-domain , such as the DCT and wavelet transform domain . Finally , current prototype status is described . 2 . Extracting Low-Level Visual Features ? Texture , Color , and Shape Feature extraction and pattern recognition has been extensively studied for decades . However , application to content-based image indexing and query is relatively new . In the context of VIS applications , new constraints and requirements have been imposed . First , image query and retrieval is basically based on similarity matching , instead of exact matching or classification . Exact matching has been typically used in information retrieval for traditional text information systems . Classification has been a typical task in computer vision and robotics where prior knowledge of finite expected objects is available . In the VIS context , image matching is based on ranking of similarity between all candidate images and the input image sample . The number of image classes is generally unlimited , except for specific constrained applications . Secondly , as described earlier , feature extraction for image database applications needs to take into account other required image technologies , such as image compression . Many image compression techniques actually have already performed some functions of information filtering and signal decomposition that may be very suitable for feature definition . Therefore , it provides tremendous advantages if visual features can be extracted from the compressed domain directly . Note that besides the above mentioned advantage , the compressed-domain approach also greatly FIGURE 1 . Concept of Compressed-Domain Image Manipulation and Feature Extraction . Images Compressed Domain -LRB- DCT , Wavelet , JPEG , MPEG , ... -RRB- Image Manipulations -LRB- filtering , multi-object compositing , transcoding , ... -RRB- Feature Extraction -LRB- texture , shape , color , motion , ... -RRB- P-3 reduces the required computational cost , since compressed images do not need to be decoded back to the uncompressed form . Third , although accurate object segmentation will be very useful for image indexing if it is robust and efficient , current image processing technologies still fall far behind this goal . However , for the purpose of image indexing and retrieval , we think that fully accurate object segmentation may not be essential . We think it will suffice to some extent if ? prominent regions ? with ? distinctive features ? can be extracted and indexed . For example , regions with clear , distinctive textures may well point to textiles , terrains , and biological tissues . The important point is that these regions do not have to correspond to real objects . Based on this region-based low-level feature extraction approach , we hope to achieve the optimal comprise between automatic mechanisms and image content extraction . Finally , future large VIS requires efficient data structure and feature comparison techniques in order to provide reasonable responsiveness to image query . Exhaustive search of every candidate image in a huge image database simply can not be acceptable . Also , the first constraint mentioned above makes it difficult to find a data structure for indexing because of the similarity matching and the high dimensionality . With the above considerations , we describe in this section techniques for extracting visual features like texture , shape , and color . We also describe the technique using a modified quad-tree data structure to bind these different visual features . 2.1 Texture Texture is an important element to human vision . Textures may be used to describe content of many real-world images ; for example , clouds , trees , bricks , hair , fabric all have textural characteristics . Particularly when combined with color and shape information , the details of image objects important for vision are provided -LSB- 19 -RSB- . By identifying textural contents of images in the database , a user may search through large volumes of images using a texture key . A ? Query-by-texture ? can be formulated to search through the image database , returning images found to contain regions of similar texture . To accomplish this task , we need to solve two problems , first : how to differentiate different textures -LRB- texture discrimination -RRB- and how to localize image regions with distinctive textures -LRB- texture region extraction -RRB- . Texture Discrimination : Techniques for describing textures can be roughly divided into two categories ? statistical and structural . Another dimension for classifying texture discrimination techniques is feature-based vs. model-based -LSB- 31 -RSB- . Ad hoc methods also exist for describing textures , such as fractals . Psychophysical studies have shown that humans perceive textures by decomposing signals into components with different frequency and orientation . Gabor filter banks have been used to approximate the mechanisms of human vision in texture discrimination -LSB- 20,21 -RSB- . However , Gabor filter banks are neither complete nor orthogonal . In order to explore the maximum synergistic relationship between texture indexing and image compression , we use the feature sets defined in transform decomposition to approximate the feature extracted from Gabor filter banks . Transform decomposition of images can be obtained by taking discrete cosine transform -LRB- DCT -RRB- , subband transform , or wavelet transform . From the decomposed signal bands , texture feature sets are extracted by measuring the subband energy . For example , for a 5-level wavelet decomposition , feature vectors with 16 terms are produced . Figure 1 illustrates the texture feature extraction procedure . In order to get texture classes as complete as possible in our experimentation , we obtain the complete set of 112 Brodatz texture images -LSB- 9 -RSB- . We hope that using the complete set of Brodatz textures in training will construct a discriminant function general enough to discriminate between new and unknown textures . From the feature sets defined above , we use the Fisher Discriminant Analysis technique to achieve the maximum average separation among different texture classes -LSB- 22 -RSB- . The Mahalanobis distance -LRB- EQ 1 -RRB- in the transformed feature space -LRB- i.e. after Fisher Discriminant Analysis -RRB- was used to measure the similarity between textures . In ordinary classification of textures or comparisons of many textures , the relative ranking of the Mahalanobis distances is used to identify closest matches . In response to a ? Query-by-texture , ? all textures in the database will be sorted by distance from the texture-key . Using the Fisher Discriminant Analysis technique , we were able to capture the feature elements with the maximum capability of texture separation . The criterion is to maximize class separability by calculating several scatter P-4 matrices , W -LRB- within-class -RRB- , B -LRB- between class -RRB- , and their ? ratio ? W-1B . Feature elements are mapped to the eigenvectors with the maximum separability significance -LRB- i.e. eigenvalues of W-1B -RRB- . The classification rule is as follows ? allocate x to class k if , , -LRB- EQ 1 -RRB- where ej is the selected eigenvectors , xi is the representative feature vector for class i. Figure 3 shows the correct classification rate versus the number of feature elements used . Even with only 6 - 8 feature elements , the classification rate still can maintain at the 90 % level . The experiments were done for a Brodatz texture database populated from the original Brodatz test set . From each Brodatz class , about 20 image cuts with random size and position were generated . This resulted in a texture database with more than 2000 texture images from the complete set of Brodatz texture collection . One of our research objectives was to evaluate the suitability of different compression algorithms . Figure 3 also shows a comparison of different compression algorithms based on the effectiveness of texture discrimination . It can be seen that the wavelet transform has the highest classification rate compared to the uniform subband and the DCT/Mandala transform1 . The energy leakage problem associated with the DCT transform may be used to 1 . DCT/Mandala subbands can be obtained by re-ordering the DCT coefficients based on the coefficient ordinates -LSB- 32 -RSB- . It can be interpreted as DCT followed by a polyphase transform as well . Minimum Distance Classifier Input Image s02 s12 s22 s32 s42 s52 s62 s72 s82 s92 -LCB- s02 , ... , sj2 -RCB- Transform Filter Bank Transform Decomposed Feature Extraction FIGURE 2 . Texture feature extraction in the wavelet domain . -LCB- sj2 -RCB- = -LCB- subband energy -RCB- Subbands for all i k ? e ' j x xk ? -LRB- -RRB- -LRB- -RRB- 2 j 1 = s ? e ' j x xi ? -LRB- -RRB- -LRB- -RRB- 2 j 1 = s ? ? Classification vs. Number of Discriminant Functions by Transform Type Number of Discriminant Functions Correct Classification % 10 20 30 40 50 60 70 80 90 100 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 Wavelet Subband Uniform Subband DCT/Mandala - block FIGURE 3 . Effect of the number of feature elements used for classification on classification rate for the Brodatz texture database . P-5 explain its inferiority in texture discrimination . However , the fact that DCT has been used popularly and commercially in many international image coding standards may still make DCT an attractive solution . Modified Quad-Tree Based Texture Region Segmentation : To extend the texture-based query approach to arbitrary local object search -LRB- as opposed to full image matching only -RRB- , images in the database are segmented into textural regions , each of which has homogeneous texture features . The discriminant function mentioned above is used to match neighboring blocks within each image to perform the texture segmentation . Because the goal of this segmentation is to provide indexing of images , we relax the constraint that the segmentation provide perfect boundary extraction . Our texture segmentation is successful when texture regions of the images are represented accurately enough to provide the expected matches upon a ? Query-by-texture ? . More accurate boundary information can be obtained in a boundary-sensitive feature such as shape , as described later . Using the spatial quad-tree approach , each quad-tree node points to a block of image data . Children nodes are merged when the discriminant function indicates that the children blocks contain sufficiently similar textures . To decide whether two textures are similar or not , we have established some optimal thresholds . We have also modified the quad-tree structure to allow each parent node to have two , three or four children . When all four children can not be merged together , subsets of the children may be paired horizontally or vertically depending on which arrangements group the most similar children . The quad-tree based region extraction process is also illustrated in Figure 4 . In the task of texture classification and discrimination , the above discriminant function is used to find the texture class with the highest similarity with the input texture key . No thresholds are required for decision making . However , for texture region extraction , the optimal texture distance threshold is required in order to merge neighboring image blocks which have sufficient similarity . Choosing optimal distance threshold is not trivial . A fixed distance threshold will not be optimal for all types of textures . From the experiment results with the Brodatz texture database , we found that the optimal distance threshold is correlated to the energy of the transform feature vector , but negatively correlated to the image block size -LSB- 2 -RSB- . The envisioned texture-based image indexing technique is to use the extracted texture regions and their attributes as image content indices . Figure 5 shows examples of the texture regions we have extracted from two natural images . Once we have these regions extracted , we can also index their derived attributes such as coordinates , size , orientation , and spatial relations among different regions . In real applications of texture-based query , texture sample keys are supposed to be provided as examples from users . Simple editing/cutting tools may be used to select arbitrary regions from existing displayed images . Another approach is to provide texture synthesis tools which will allow users to adjust texture description/synthesis parameters . Currently , we are testing the usefulness of this texture-based indexing/query technique in practical applications such as satellite picture databases , medical image databases , and art image archives . -LRB- a -RRB- -LRB- b -RRB- FIGURE 4 . -LRB- a -RRB- Modified Spatial Quad-tree representation -- each tree parent can have two , three or four children , -LRB- b -RRB- Texture-Based Quad-tree segmentation of the Barbara image . Each Quad-tree node is indicated by white bordered region . P-6 2.2 Color Psychophysical research has been studying how human vision system discriminates different colors . There has been research applying color features as query keys to image database applications . Binaghi et al. . have developed a system which uses only color as the index into a database of color fabric samples -LSB- 8 -RSB- . Niblack et al. includes color feature in their QBIC system -LSB- 24 -RSB- . Swain et al. used color indices for 3D object classification -LSB- 23 -RSB- . Color-based indexing provides several unique advantages . It?s less sensitive to noise and background complication , compared to other features such as shape and textures . Also , it is independent of image size and orientation . However , it also has disadvantages such as high sensitivity to illumination and shade . Furthermore , the choice of color representations -LRB- i.e. , color space -RRB- is very critical . Different color spaces will result in different color dynamic ranges , different color differentiation capability . The popular RGB color space is efficient for display , but inappropriate for color feature indexing and discrimination . One important criterion we use for selecting the color space is based on the color distance uniformity , which means that the physical distance in the color space is proportional to the subjective perception distance . We have chosen the Lu * v * and La * b * color spaces as the basis for color representation . However , there are still drawbacks for using these uniform color spaces like Lu * v * . The main problem is that the translation process from RGB space to the Lu * v * is complicated and , more importantly , the reverse translation process is very difficult . Usefulness of color-based features is still an open issue requiring more research . A technique based on color histograms is used in the QBIC system -LSB- 24 -RSB- . From the color histograms , other color - related features can be derived , such as average color , color intersection and color pairs . Color intersection was used in -LSB- 23 -RSB- to perform 3D object classification . Color pairs were used in -LSB- 13 -RSB- to capture the spatial correlations between adjacent color regions . Extension of color pairs was reported in -LSB- 33 -RSB- to reduce the effect of background images on the accuracy of the color pairs . In our system , we are taking two separate approaches ? single color region extraction and quad-tree based color histogram indexing . We describe them separately in the following . Single Color Region Extraction : A simple but useful method using the image color features to capture the visual content is single color region extraction . First , we select a finite set of representative colors . For each representative color , we find the image 1 2 3 4 5 1 2 3 5 6 4 FIGURE 5 . The results of modified quad-tree based texture region extraction . Significant texture regions with homogeneous texture content are abstracted out as the indices of images in the database . P-7 regions whose pixels are within some small distance from the representative color . Once we have these color regions extracted , the file inversion approach of data indexing is used to store these single color regions . Figure 6 illustrates the single color data structure . Based on these single color regions , more sophisticated image queries can be formulated , such as those concerning the location , size , spatial relations of the color regions . Given the color key from users , scanning of the raw image data is no longer necessary . Instead , efficient search against the single color list can be utilized . Several technical issues need to be resolved . Selection of the representative colors will determine the gamut range of the whole application . It needs to be optimized with joint consideration of computational efficiency . For specific applications , the representative color list can be obtained by training and clustering . Most typical colors can thus be measured and recorded . A general approach is to simply quantize a uniform color space such as the Lu * v * color space . For initial general testing , we have adopted the later approach . Extraction of contiguous regions corresponding to each color can be done in different ways also . Each image in the database can be quantized against the representative color list , with appropriate distance functions defined in each color space . For example , a Euclidean distance measure may be sufficient in the uniform color space . This method will result in non-overlapping color regions in each image , with hard region boundaries among different colors . To take into account of fuzziness in our subjective perception , we adopted an alternative which achieves soft region boundaries at some cost of higher computational complexity . The computational process is as follows . For any given representative color , the distance between every pixel in the image and the sample color is calculated . The results are then stored in a distance image which has the same size as the original image . Non-linear rank filters are used to remove spot noise and improve region smoothness . The filtered distance image is then thresholded with some threshold level which can be set to be specific to each different color . A second threshold is also used to removed small regions at the end . Soft overlapped region boundaries are achieved by using adaptive distance thresholds for different colors . Also , this technique can be used independently of the choice of the color space and the distance function . Quad-Tree Based Color Histogram Indexing : Color histogram is supposed to capture much richer information than the single color approach . Color histograms are defined based on an image region . Other useful color information such as dominant color components and average colors can all be derived from the color histograms . However , once the raw image data is reduced to the color histogram , the spatial information is lost completely . To overcome this problem , we apply and extend the quad-tree based approach used in the texture indexing approach to the color histogram . Starting from the quad-tree spatial decomposition of an image , we measure the individual color histogram of each quad-tree terminal node , which corresponds to the smallest image unit . The size of the image unit should be determined based on the optimal tradeoff between the spatial resolution and color histogram reliability . Using an approach similar to the texture region segmentation , neighboring quad-tree nodes are compared and similar blocks are merged . One important issue is how to measure the color histogram distance . The discussion of uniform color space which has uniform subjective color distance can not be directly extended to the color histogram . The Euclidean distance between two color histogram vectors is not suited in this case . In -LSB- 24 -RSB- , a modified distance function was proposed to overcome this problem . The distance between two histograms and can be defined as Single color list : -LCB- red , pink , orange , ... -RCB- ... FIGURE 6 . An inversion file approach for single color indexing . x y P-8 -LRB- EQ 2 -RRB- where elements of matrix A represents the ? cross correlation ? between color i and color j . If A is the identity matrix , then -LRB- EQ 2 -RRB- becomes the standard Euclidean distance . The compensation by coefficients is important because usually colors are not orthogonal . For example , the distance between red and orange should be smaller than that between red and blue . Therefore , between red and orange should be smaller than between red and blue . Using color histograms to discriminate colors actually provides much flexibility . For example , the average color of an image region can be obtained easily by calculating the mean of the color histogram . In addition , as described in -LSB- 23 -RSB- , intersections of color histograms can provide useful measurement of color similarity , such as ? 30 % of the color distribution of image A is similar to the color distribution of image B ? . The color pair approach used in -LSB- 13 -RSB- was also based on the color histogram platform . However , none of the previous approaches explored the possibility of abstracting prominent regions based on the color histogram feature . 2.3 Shape Shape feature is extremely useful in many image databases -LRB- such as electronics schematic matching -RRB- and pattern matching applications -LRB- such as military target recognition -RRB- . Wavelet decomposition has been used to effectively detect edges in images . Mallat and Zhong used the derivative of Gaussian as the wavelet function and detect edge points at the maxima or zero-crossing points in different scales -LSB- 26,25 -RSB- . The same technique has been applied to signal approximation and image coding as well -LSB- 26 -RSB- . Related work of multi-scale edge detection has also been proposed by Canny in -LSB- 27 -RSB- . These prior efforts have provided an excellent framework for shape extraction in the wavelet domain , based on the assumption that shape information can be obtained by linking edge points in some smart ways . In -LSB- 30 -RSB- , high-level reasoning techniques combining the specific domain knowledge were proposed to abstract object shapes from the underlying edge points . We think for practical applications the integration with domain knowledge is not only unavoidable , but also very beneficial . In our study , we also combine the wavelet domain edge detection technique with the previously mentioned quad-tree based segmentation . There are two major advantages that the quad-tree-based technique can bring to edge detection in the wavelet domain . The first is for post-processing the edge information , in particular , ? closing ? and ? tracing ? scattered broken edges in different scales . Instead of globally tracing edge points with some smoothness constraints , a ? split and link ? approach based on the quad-tree decomposition can be used . Within each image block pointed by a quad-tree node , we perform local closing operation to produce connected edges . Then each edge is registered by its interception points with the block boundaries . To link edge segments from neighboring blocks , we compute the distance between interception points on the overlapping boundary . Edge segments from neighboring blocks are linked together if this distance is below some threshold . By using this ? split and link ? approach , edge closing operations are performed locally within an image block only , and thus the computational complexity can be reduced . The second benefit of using the quad-tree based image decomposition is to bind the edge information with other features , such as texture and color , in the same data structure . We discuss this unexplored area in the next section . 3 . An Integrated Feature Map for Content-Based Visual Query The segmentation results from texture-based and color-based segmentation are region-based . In other words , each terminal node in the final tree structure represents an image region with homogeneous features -LRB- color or texture -RRB- . The edge-based feature extraction produces closed or broken shapes . Integration of these final features in the same quad-tree structure of an image is very intriguing . Figure 7 illustrates the concept of integrated feature map . Regions produced from texture and color segmentations and shapes produced from edge detection are mapped onto the same quad-tree data structure . This can be easily implemented since we have used the same quad-tree structure in texture segmentation , color segmentation , and edge segment linking . There are many great potential applications of this integrated feature map . d x y , -LRB- -RRB- x y ? -LRB- -RRB- tA x y ? -LRB- -RRB- x i y i ? -LRB- -RRB- ai j , xj yj ? -LRB- -RRB- i j , ? = = ai j , ai j , a i j , ai j , P-9 First , independent signal features can now be integrated to detect and index image regions/objects more effectively . Some objects may not be detectable by one single feature only . Consistency in segmentation results from different features will improve the confidence level of the object extraction results . Also , by incorporating domainspecific knowledge systems , we may be able to map the low-level feature maps to real-world objects in constraint applications . Indexing each image object by multiple features also provides higher flexibility in the query stage . For example , the same object can be searched by any or all of the associated features . A query example could be ? find images containing regions with this texture AND/OR this color pattern AND/OR this shape ? . Secondly , boundary alignment between objects extracted by different features provides a new arena of image query . Some image regions may have well-aligned segmentation boundaries from different features . Some objects may not , depending on the physical appearance of the image objects . For example , with the proposed integrated feature map , the image database will allow users to issue queries like ? find image regions with this texture , this homogeneous color pattern in the center , but a random color distribution around the boundary ? , or ? find all image regions with homogeneous texture and well enclosed color patterns within the boundary -LRB- as opposite to , a color pattern spread over several texture regions -RRB- ? . In the multi-scaled edge-extraction techniques , uncertainty still exists especially when the input image is noisy or there are strong interactions between close edges . Some shapes may remain broken after the closing operation . With the integrated feature map , this problem can be alleviated to some extent . Inference from segmentation results from other features , such as color and texture , can improve the edge accuracy and find the missing edge segment of an image object . For example , as shown in Figure 7 , if there is indication of a large major object from other features , the missing edge segment can be supplemented by the boundary from the color/texture segmentation . More importantly , based on the unified quad-tree data structure of the image , it is easy to perform the above inference and find the image blocks where missing edge segments are supposed to be found or interpolated . 4 . Prototype and Testbed Part of our work on integrated investigation of image feature extraction , compression , and interactive manipulations has been incorporated into Columbia?s Multimedia/Video-on-Demand Testbed , for which the first generation has been completed -LSB- 17 -RSB- . The testbed is intended to accommodate various multimedia-oriented research and development projects in the campus . Current large-scale projects undertaken include video-on-demand , distributed visual information systems , and medical image databases . Interoperability experiments , which allow interconnections with outside VOD testbeds and high-speed networks , have been initiated as well . We have implemented prototypes for each individual visual feature . A Brodatz texture image database supporting texture-based discrimination is shown in Figure 8 . Multi-resolution incremental retrieval of matched images is supported . Figure 9 shows the interactive system interface which allows users to navigate through different color spaces , to formulate desirable single color key , and to specify the color dissimilarity threshold levels in the single color retrieval mechanism . Formulation of color histogram queries can be supported by simple image cutting and editing tools . Currently , we are in the stage of integrating all these individual visual features to practical applications such as art image databases -LSB- 34 -RSB- , medical image databases , and video-on-demand . For video indexing , we take a top-down approach to segment the whole image sequence into different scenes . Each video scene is assumed to have consistent visual feature content . Therefore , a representative image frame from each scene is sufficient to capture the rough con - -LRB- a -RRB- Texture -LRB- b -RRB- Color -LRB- c -RRB- Edge/Shape FIGURE 7 . Integrating segmentation results based on different features -LRB- a -RRB- texture -LRB- b -RRB- color -LRB- c -RRB- edge/shape . P-10 tent for the entire scene . All the above signal features like texture , color , and shape can then be applied to index these representative image frames . In -LSB- 35 -RSB- , we have reported a Video Indexing system supporting automatic scene change / dissolve detection in the MPEG compressed domain . 5 . Conclusions and Future Work Visual feature based image query has opened a new area calling for advanced study of image understanding , image database indexing , and fast compressed image processing . In anticipation of future large visual information systems , exploration of maximum synergistic optimization between image feature extraction , image compression , and storage optimization needs to be pursued . In this paper , we describe one of our current efforts in using multi-dimen - -LRB- a -RRB- -LRB- b -RRB- FIGURE 8 . Texture Discrimination System . -LRB- a -RRB- Interface Showing Texture Keys , -LRB- b -RRB- Results of Query-byTexture using Brodatz texture 23 as the texture-key . -LRB- a -RRB- -LRB- b -RRB- FIGURE 9 . -LRB- a -RRB- Color Space Navigator ? a tool that allows users navigate through 3-D color space ; the 6x6 color pane displays local space . -LRB- b -RRB- Color Agent ? another tool that allows user to define single color query by using text description and multiple color space navigation . P-11 sional low-level signal features in image indexing and query . By stressing at the low-level features and using less stringent segmentation requirement , we hope to keep the indexing process automatic , without expensive user interventions . We also hope to minimize the implementation complexity by deriving signal features directly in the compressed domain , such as DCT , wavelet transform , and the MPEG domain . In addition , we proposed a modified quadtree data structure for binding different signal features . Technical barriers still exist in the way to fully automatic content extraction -LRB- ideally -RRB- in the compressed domains . One example is the difficulty in interpreting subjective color perception in the compressed domain . Unlike edge or texture which are more or less amenable to compression algorithms , color information is hard to capture in the compressed domain . 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O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O 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O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O B O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O O O O O O O O O O O O B B 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B O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O B O O O O O O O O O O O B O O B O O O O O O O O O O O B O O O O B O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B O O O O O B O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O B O O O O O O O O B O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O B I O O O O O O O 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O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O B O O O O O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O B O O O O O O O O B O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O 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