Parametric texture models have been applied successfully to synthesize artificial images. Psychophysical studies show that under defined conditions observers are unable to differentiate between model-generated and original natural textures. In industrial applications the reverse case is of interest: a texture analysis system should decide if human observers are able to discriminate between a reference and a novel texture. For example, in case of inspecting decorative surfaces the de- tection of visible texture anomalies without any prior knowledge is required. Here, we implemented a human-vision-inspired novelty detection approach. Assuming that the features used for texture synthesis are important for human texture percep- tion, we compare psychophysical as well as learnt texture representations based on activations of a pretrained CNN in a novelty detection scenario. Additionally, we introduce a novel objective function to train one-class neural networks for novelty detection and compare the results to standard one-class SVM approaches. Our experiments clearly show the differences between human-vision-inspired texture representations and learnt features in detecting visual anomalies. Based on a dig- ital print inspection scenario we show that psychophysical texture representations are able to outperform CNN-encoded features. The idea of describing the appearance of textures using statistics goes back to the early work by Gibson BID0 BID5 and by Julesz BID10 BID11 BID12 . Since then, a number of Markov random field texture models for modelling and characterizing textures using the statistical description of local neighbourhoods were introduced by BID1 and BID4 . Another category of models tries to find a plausible texture representation for the early visual system of humans BID7 BID14 BID16 . These human-vision-inspired models are based on a decomposition of the texture to frequency and orientation bands. The well-known texture model by BID14 (PS-model) and the recently published image-computable spatial vision model by (SW-model) are two representatives of such psychophysical models. The PSmodel is based on joint statistics of complex wavelet coefficients BID21 and focuses on synthesizing realistic textures. The SW-model is based on a log-Gabor decomposition followed by an accelerating nonlinearity and normalization. In contrast to these psychophysically motivated models (assuming a plausible image representation for the early visual system), BID2 introduced a texture model based on features of a pretrained deep convolutional neural network (CNN). Using these CNN-encoded features, the model shows impressive results in generating artificial textures BID3 BID24 .Specific texture representations are required in industrial applications, e.g. for pattern recognition tasks or modelling human perception. In this work we focus on representing textures in such a way that visual anomalies can be detected when comparing reference and novel examples. This is particularly required whenever a reference texture should be reproduced visually indistinguishable. In general , the task of identifying data that differs from a reference is known as novelty detection. In contrast to classification tasks, only one class of labelled data (reference texture) is available. As an example of application, we use images of artificial wood textures which we digitised using line-scanner cameras, installed in an industrial digital printer for laminate flooring. Here, the reference texture is only available digitally as a scanned image of the initially produced reference decor. From a machine learning point of view, there are no labelled training samples available to train a supervised classifier. Another critical point is the interdependency of the surrounding texture and the visibility of specific anomalies. Hence, isolated anomaly consideration is not feasible (cf. FIG0 . In decorative surfaces , e.g. , a wallpaper of a printed brick wall or a cupboard with an artificial wood texture, each print should be indistinguishable from any other. Since humans perceive small variations in a texture when comparing two examples, the detection of visual anomalies perceived by a human observer is an open challenge in computer vision and a continuous problem in industrial applications.To learn the model of a reference texture and detect visual anomalies in novel examples, we present an unsupervised one-class neural network approach using a parametric texture representation. We summarize our main contributions as follows:• We introduce a novel objective function (MinMax-loss) to train one-class neural networks for novelty detection in textures.• We introduce psychophysically motivated texture models -PS-model and SW-model -for novelty detection using one-class classifiers and provide a comparison with deep CNN texture features.• We show texture dependent differences between the models which are of interest in visual surface inspection applications. In this paper, we evaluated the performance of novelty detection in digital print inspection using psychophysical and learnt texture representations. First we introduced state-of-the art methods for statistical and early vision based modelling of textures. While the model by BID14 focuses on synthesizing realistic textures, the model by focuses on modelling the human early vision system in an image-computable way. Another branch of texture modelling uses learnt representations based on features of a pretrained CNN BID2 . Based on the aforementioned features we learnt the reference model of a texture and detected visual anomalies in novel examples. Therefore, we introduced a novel objective for training neural network based one-class classifiers for novelty detection (OC-NN). Additionally, we compared our OC-NN approach with an OC-SVM based classifier and showed superior results in our application scenario.All texture representations achieve reasonable results, when being evaluated with our OC-NN approach on a quasi-periodic texture (cf. red-bricks). However, when being evaluated by an OC-SVM based classifier, anomalies cannot be detected using SW-as well as VGG-19-features. This might be due to the lack of preprocessing, such as z-normalisation or whitening, but this is referred to future work. Altogether the learnt texture representations provide a set of features for detecting novelties, that performs well, whether or not they are perceived by a human observer. Furthermore, PS-features provide a texture-dependent descriptor, that achieves reasonable results on quasi-periodic textures.Finally the SW-features outperform the PS-and VGG-19-features for small anomalies on aperiodic textures, which is great from the application point of view. However, when anomalies dominate (fixed patch size and increasing anomaly size), the detection rate decreases.As a part of the work for this paper, we evaluated our one-class model for novelty detection on a broad range of industrial printed decors, but more work using psychophysical data from experiments is needed. We plan to validate selected anomalies in psychophysical experiments. Further work will also include evaluating different pooling and averaging methods for SW-features. Finally, we plan to fine-tune an OC-SVM for being able to use SW-features.
Comparison of psychophysical and CNN-encoded  texture representations in a one-class neural network novelty detection application. This paper focuses on novelty detection and shows that psychophysical representations can outperform VGG-encoder features in some part of this task This paper considers detecting anomalies in textures and proposes original loss function. Proposes training two anomaly detectors from three different models to detect perceptual anomalies in visual textures.