Detecting Anomalies in Image Classification by Means of Semantic Relationships

Pasini, Andrea; Baralis, Elena

doi:10.1109/aike.2019.00047

Cited by 8 publications

(7 citation statements)

References 20 publications

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“…They used deep learning methods to train the controller and help understand system dynamics in real-time through expert knowledge represented by rules. Pasini and Baralis 144 proposed a semantic anomaly detection method. By learning semantic information from the training set and storing it in the form of configuration rules in the knowledge base, anomalies in the prediction of any pixel semantic segmentation algorithm can be detected.…”

Section: Rule-based Methodsmentioning

confidence: 99%

Knowledge modeling: A survey of processes and techniques

Yun

Zhang

et al. 2021

Int J Intell Syst

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Section: Rule-based Methodsmentioning

confidence: 99%

Knowledge modeling: A survey of processes and techniques

Yun

Zhang

et al. 2021

Int J Intell Syst

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show abstract

“…Zhang et al use the semantic context information, such as motion pattern and path, to improve abnormal event detection in traffic scenes where an abnormal event is defined as vehicles breaking the traffic rules by considering the trajectories [33]. Pasini et al present a semantic anomaly detection method to detect anomalies and provides an interpretable explanation [34]. They construct the semantic vector from the textual labels obtained from the pre-trained image labeling software.…”

Section: Related Workmentioning

confidence: 99%

An explainable and efficient deep learning framework for video anomaly detection

Shao

Tunc

et al. 2021

Cluster Comput

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“…Zhang et al use the semantic context information, such as motion pattern and path, to improve abnormal event detection in traffic scenes where an abnormal event is defined as vehicles breaking the traffic rules by considering the trajectories [21]. Pasini et al present a semantic anomaly detection method to detect anomalies and provides an interpretable explanation [22]. They construct the semantic vector from the textual labels obtained from the pre-trained image labeling software.…”

Section: Related Workmentioning

confidence: 99%

Video Anomaly Detection using Pre-Trained Deep Convolutional Neural Nets and Context Mining

Shao

Tunc

et al. 2020

2020 IEEE/ACS 17th International Conference on Computer Systems and Applications (AICCSA)

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Anomaly detection is critically important for intelligent surveillance systems to detect in a timely manner any malicious activities. Many video anomaly detection approaches using deep learning methods focus on a single camera video stream with a fixed scenario. These deep learning methods use large-scale training data with large complexity. As a solution, in this paper, we show how to use pre-trained convolutional neural net models to perform feature extraction and context mining, and then use denoising autoencoder with relatively low model complexity to provide efficient and accurate surveillance anomaly detection, which can be useful for the resource-constrained devices such as edge devices of the Internet of Things (IoT). Our anomaly detection model makes decisions based on the high-level features derived from the selected embedded computer vision models such as object classification and object detection. Additionally, we derive contextual properties from the high-level features to further improve the performance of our video anomaly detection method. We use two UCSD datasets to demonstrate that our approach with relatively low model complexity can achieve comparable performance compared to the state-of-the-art approaches.

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Detecting Anomalies in Image Classification by Means of Semantic Relationships

Cited by 8 publications

References 20 publications

Knowledge modeling: A survey of processes and techniques

Knowledge modeling: A survey of processes and techniques

An explainable and efficient deep learning framework for video anomaly detection

Video Anomaly Detection using Pre-Trained Deep Convolutional Neural Nets and Context Mining

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