Novelty detection in images by sparse representations

Boracchi, Giacomo; Carrera, Diego; Wohlberg, Brendt

doi:10.1109/inteles.2014.7008985

Cited by 29 publications

(51 citation statements)

References 20 publications

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“…The same test was done after adding a small anomalous spot to the noise, and the conclusion is similar: [33,56] perform well, [9] has a couple of false detections and doesn't detect the anomaly. One method, Zontak and Cohen [153], doesn't detect anything.…”

Section: Qualitative Evaluationmentioning

confidence: 87%

“…Davy et al [33], and Grosjean and Moisan [56] soundly detect no anomaly in white noise, as expected. However a few detections are made by Boracchi et al [9] and almost everything is detected by Mishne and Cohen [96]. It can be noted that the background model of the first three papers is directly respected in the case of white Gaussian noise, which explains the perfect result.…”

Section: Qualitative Evaluationmentioning

confidence: 96%

“…The Boracchi et al [9] method is directly related to the sparse texture modeling previously introduced by Elhamifar et al [45], where a "row sparsity index" is defined to distinguish outliers in a dataset. The outliers are added to the dictionary.…”

Section: Sparsity-based Background Models and Its Variational Implemementioning

confidence: 99%

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Image Anomalies: A Review and Synthesis of Detection Methods

et al. 2019

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We review the broad variety of methods that have been proposed for anomaly detection in images. Most methods found in the literature have in mind a particular application. Yet we focus on a classification of the methods based on the structural assumption they make on the "normal" image, assumed to obey a "background model". Five different structural assumptions emerge for the background model. Our analysis leads us to reformulate the best representative algorithms in each class by attaching to them an a-contrario detection that controls the number of false positives and thus deriving a uniform detection scheme for all. By combining the most general structural assumptions expressing the background's normality with the proposed generic statistical detection tool, we end up proposing several generic algorithms that seem to generalize or reconcile most methods. We compare the six best representatives of our proposed classes of algorithms on anomalous images taken from classic papers on the subject, and on a synthetic database. Our conclusion hints that it is Both authors contributed equally to this work. possible to perform automatic anomaly detection on a single image.

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Section: Qualitative Evaluationmentioning

confidence: 87%

Section: Qualitative Evaluationmentioning

confidence: 96%

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Image Anomalies: A Review and Synthesis of Detection Methods

et al. 2019

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“…Patch-based sparse models have been recently used for anomaly detection purposes [12], where an unconstrained optimization problem is solved to obtain the sparse representation of each patch in a test image. Then, the reconstruction error and the sparsity of the computed representation are jointly monitored to detect the anomalous structures.…”

Section: Related Workmentioning

confidence: 99%

“…• Patch-based: a standard sparse model [21] rather than a convolutional sparse model is used to describe patches extracted from s h , as in [12]. The indicator includes the reconstruction error and the sparsity of the representation.…”

Section: Considered Algorithmsmentioning

confidence: 99%

Detecting anomalous structures by convolutional sparse models

Carrera

Boracchi

Foi

et al. 2015

2015 International Joint Conference on Neural Networks (IJCNN)

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Abstract-We address the problem of detecting anomalies in images, specifically that of detecting regions characterized by structures that do not conform those of normal images. In the proposed approach we exploit convolutional sparse models to learn a dictionary of filters from a training set of normal images. These filters capture the structure of normal images and are leveraged to quantitatively assess whether regions of a test image are normal or anomalous. Each test image is at first encoded with respect to the learned dictionary, yielding sparse coefficient maps, and then analyzed by computing indicator vectors that assess the conformance of local image regions with the learned filters. Anomalies are then detected by identifying outliers in these indicators.Our experiments demonstrate that a convolutional sparse model provides better anomaly-detection performance than an equivalent method based on standard patch-based sparsity. Most importantly, our results highlight that monitoring the local group sparsity, namely the spread of nonzero coefficients across different maps, is essential for detecting anomalous regions.

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