Survey on Sparse Coded Features for Content Based Face Image Retrieval
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paper_title: Robust Face Recognition via Sparse Representation

paper_content:

We consider the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise. We cast the recognition problem as one of classifying among multiple linear regression models and argue that new theory from sparse signal representation offers the key to addressing this problem. Based on a sparse representation computed by C1-minimization, we propose a general classification algorithm for (image-based) object recognition. This new framework provides new insights into two crucial issues in face recognition: feature extraction and robustness to occlusion. For feature extraction, we show that if sparsity in the recognition problem is properly harnessed, the choice of features is no longer critical. What is critical, however, is whether the number of features is sufficiently large and whether the sparse representation is correctly computed. Unconventional features such as downsampled images and random projections perform just as well as conventional features such as eigenfaces and Laplacianfaces, as long as the dimension of the feature space surpasses certain threshold, predicted by the theory of sparse representation. This framework can handle errors due to occlusion and corruption uniformly by exploiting the fact that these errors are often sparse with respect to the standard (pixel) basis. The theory of sparse representation helps predict how much occlusion the recognition algorithm can handle and how to choose the training images to maximize robustness to occlusion. We conduct extensive experiments on publicly available databases to verify the efficacy of the proposed algorithm and corroborate the above claims.
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paper_title: Unsupervised auxiliary visual words discovery for large-scale image object retrieval

paper_content:

Image object retrieval–locating image occurrences of specific objects in large-scale image collections–is essential for manipulating the sheer amount of photos. Current solutions, mostly based on bags-of-words model, suffer from low recall rate and do not resist noises caused by the changes in lighting, viewpoints, and even occlusions. We propose to augment each image with auxiliary visual words (AVWs), semantically relevant to the search targets. The AVWs are automatically discovered by feature propagation and selection in textual and visual image graphs in an unsupervised manner. We investigate variant optimization methods for effectiveness and scalability in large-scale image collections. Experimenting in the large-scale consumer photos, we found that the the proposed method significantly improves the traditional bag-of-words (111% relatively). Meanwhile, the selection process can also notably reduce the number of features (to 1.4%) and can further facilitate indexing in large-scale image object retrieval.
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paper_title: Linear spatial pyramid matching using sparse coding for image classification

paper_content:

Recently SVMs using spatial pyramid matching (SPM) kernel have been highly successful in image classification. Despite its popularity, these nonlinear SVMs have a complexity O(n2 ~ n3) in training and O(n) in testing, where n is the training size, implying that it is nontrivial to scaleup the algorithms to handle more than thousands of training images. In this paper we develop an extension of the SPM method, by generalizing vector quantization to sparse coding followed by multi-scale spatial max pooling, and propose a linear SPM kernel based on SIFT sparse codes. This new approach remarkably reduces the complexity of SVMs to O(n) in training and a constant in testing. In a number of image categorization experiments, we find that, in terms of classification accuracy, the suggested linear SPM based on sparse coding of SIFT descriptors always significantly outperforms the linear SPM kernel on histograms, and is even better than the nonlinear SPM kernels, leading to state-of-the-art performance on several benchmarks by using a single type of descriptors.
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Title: Survey on Sparse Coded Features for Content Based Face Image Retrieval

Section 1: INTRODUCTION
Description 1: Provide an overview of the problem, the scope of the survey, and the significance of using sparse-coded features for face image retrieval.

Section 2: FACE RECOGNITION VIA SPARSE REPRESENTATION
Description 2: Discuss how sparse representation addresses the challenges in face recognition and improves retrieval systems.

Section 3: SPARSE CODING FOR IMAGE CLASSIFICATION AND RETRIEVAL
Description 3: Explain the use of sparse coding in image classification and retrieval, including methods and algorithms involved.

Section 4: SPARSE CODING WITH IDENTITY CONSTRAINTS
Description 4: Explore the application of sparse coding with identity constraints to enhance face image retrieval systems.

Section 5: ATTRIBUTE ENHANCED SPARSE CODEWORDS
Description 5: Detail how high-level human facial attributes can enhance the effectiveness of sparse-coded representations in face image retrieval.

Section 6: SPATIAL PYRAMID MATCHING USING SPARSE CODING
Description 6: Examine the use of Spatial Pyramid Matching (SPM) combined with sparse coding for improved image classification performance.

Section 7: CONCLUSION
Description 7: Summarize the main findings, the overall effectiveness of sparse-coded features in face image retrieval, and potential future directions for research.