Psychophysical vs. learnt texture representations in novelty detection
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 authors describe an anomaly/novelty detection method based on handcrafted features + VGG based features. I think the paper is out of the scope of the conference (the only part dealing with learned representations uses VG), plus it addresses a problem whose relevance is not correctly motivated. Finally, the method is quite basic, and is not compared to any state of the art method for novelty detectiobn. In "... the detection of visual anomalies perceived by human observer is an open challenge… " can you provide references of people working in this particular problem? The review of related work seems obsolete, can you provide more recent references (in addition to "historical" ones). More importantly, please provide references of anomaly detection from textures
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Psychophysical vs. learnt texture representations in novelty detection
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.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 The authors describe an anomaly/novelty detection method based on handcrafted features + VGG based features. I think the paper is out of the scope of the conference (the only part dealing with learned representations uses VG), plus it addresses a problem whose relevance is not correctly motivated. Finally, the method is quite basic, and is not compared to any state of the art method for novelty detectiobn. In "... the detection of visual anomalies perceived by human observer is an open challenge… " can you provide references of people working in this particular problem? The review of related work seems obsolete, can you provide more recent references (in addition to "historical" ones). More importantly, please provide references of anomaly detection from textures
assistant
 The authors describe an anomaly/novelty detection method based on handcrafted features + VGG based features. I think the paper is out of the scope of the conference (the only part dealing with learned representations uses VG), plus it addresses a problem whose relevance is not correctly motivated. Finally, the method is quite basic, and is not compared to any state of the art method for novelty detectiobn. In "... the detection of visual anomalies perceived by human observer is an open challenge… " can you provide references of people working in this particular problem? The review of related work seems obsolete, can you provide more recent references (in addition to "historical" ones). More importantly, please provide references of anomaly detection from textures