A clustering-based deep autoencoder for one-class image classification

Gutoski, Matheus; Ribeiro, Matilde; Aquino, Nelson Marcelo Romero; Lazzaretti, André E.; Lopes, Heitor Silvério

doi:10.1109/la-cci.2017.8285680

Cited by 13 publications

(13 citation statements)

References 16 publications

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“…Thus, the rest of the samples can be considered as anomalies. This problem has been addressed in the literature through the use of the one-class versions of the SVM, k-nearest neighbor (k-NN), random forests classifiers, and more recently with deep autoencoders [9,38,53]. In our case, an autoencoder is trained to model the class of the typical FP detections.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Another approach is the one followed by Gutoski et al in which autoencoders and stacked denoising autoencoders are used for clustering [38]. With the clustering, representation in possible to define whether a new sample is an anomaly or not according to its distance to the clusters.…”

Section: Related Workmentioning

confidence: 99%

“…With the clustering, representation in possible to define whether a new sample is an anomaly or not according to its distance to the clusters. Gutoski et al also followed this approach for one-class classification [38].…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Deep autoencoder for false positive reduction in handgun detection

Vállez

Velasco-Mata

Déniz

2020

Neural Comput & Applic

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In an object detection system, the main objective during training is to maintain the detection and false positive rates under acceptable levels when the model is run over the test set. However, this typically translates into an unacceptable rate of false alarms when the system is deployed in a real surveillance scenario. To deal with this situation, which often leads to system shutdown, we propose to add a filter step to discard part of the new false positive detections that are typical of the new scenario. This step consists of a deep autoencoder trained with the false alarm detections generated after running the detector over a period of time in the new scenario. Therefore, this step will be in charge of determining whether the detection is a typical false alarm of that scenario or whether it is something anomalous for the autoencoder and, therefore, a true detection. In order to decide whether a detection must be filtered, three different approaches have been tested. The first one uses the autoencoder reconstruction error measured with the mean squared error to make the decision. The other two use the k-NN (k-nearest neighbors) and one-class SVMs (support vector machines) classifiers trained with the autoencoder vector representation. In addition, a synthetic scenario has been generated with Unreal Engine 4 to test the proposed methods in addition to a dataset with real images. The results obtained show a reduction in the number of false positives between 22.5% and 87.2% and an increase in the system’s precision of 1.2%$$-47$$ - 47 % when the autoencoder is applied.

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Deep autoencoder for false positive reduction in handgun detection

Vállez

Velasco-Mata

Déniz

2020

Neural Comput & Applic

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“…(Chalapathy et al, 2018) used the latent representation of image data produced by the encoder part of a convolutional autoencoder to train a one class neural network that outputs an anomaly score. (Gutoski et al, 2017) used deep embedded clustering in the bottleneck of the autoencoder for learning normal cluster centres and compact deep feature representation. Furthermore, Generative neural networks (GAN) (Goodfellow et al, 2014) are progressively used to solve novelty detection techniques.…”

Section: Related Workmentioning

confidence: 99%

“…We formulate the anomaly detection problem as a one class classification problem. The classifier has to recognize the anomalous patterns, based on a previous knowledge of the normal or expected patterns (Gutoski et al, 2017). The main idea is to use the reconstruction error of a convolutional autoencoder (CAE) pre-trained with normal data as patterns for the classifier.…”

Section: Proposed Approachmentioning

confidence: 99%

Usage Identification of Anomaly Detection in an Industrial Context

Zoghlami

Kurrek

Jocas

et al. 2019

Proc. Int. Conf. Eng. Des.

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The use of flexible and autonomous robotics systems is the solution for the automation task of the production and intra-logistics environments. This dynamic context requires the robot to be aware of its surroundings through the whole task, also after accomplishing the gripping action. We present an anomaly detection approach based on unsupervised learning and reconstruction fidelity of image data. We design our method to enhance the dynamic environment perception of robotics systems and apply it in a palletizing robot, in order to perceive and detect changes to its surrounding and process after the gripping step. Our proposed approach achieves the performance targeted by the considered industrial requirements.

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Vision and Crowdsensing Technology for an Optimal Response in Physical-Security

Enríquez

Soria

Álvarez-García

et al. 2019

Lecture Notes in Computer Science

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Law enforcement agencies and private security companies work to prevent, detect and counteract any threat with the resources they have, including alarms and video surveillance. Even so, there are still terrorist attacks or shootings in schools in which armed people move around a venue exercising violence and generating victims, showing the limitations of current systems. For example, they force security agents to monitor continuously all the images coming from the installed cameras, and potential victims nearby are not aware of the danger until someone triggers a general alarm, which also does not give them information on what to do to protect themselves. In this article we present a project that is being developed to apply the latest technologies in early threat detection and optimal response. The system is based on the automatic processing of video surveillance images to detect weapons and a mobile app that serves both for detection through the analysis of mobile device sensors, and to send users personalised and dynamic indications. The objective is to react in the shortest possible time and minimise the damage suffered.

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A clustering-based deep autoencoder for one-class image classification

Cited by 13 publications

References 16 publications

Deep autoencoder for false positive reduction in handgun detection

Deep autoencoder for false positive reduction in handgun detection

Usage Identification of Anomaly Detection in an Industrial Context

Vision and Crowdsensing Technology for an Optimal Response in Physical-Security

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