Multi-view Outlier Detection in Deep Intact Space

Ji, Yuxuan; Huang, Ling; He, Heng-Ping; Wang, Chang‐Dong; Xie, Guangqiang; Wei, Shi; Lin, Kun-Yu

doi:10.1109/icdm.2019.00136

Cited by 21 publications

(12 citation statements)

References 28 publications

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“…This contribution focuses on how to fuse information for multi-perspective anomaly detection. Multiple perspectives or views are generally associated with data obtained from different modalities [15]. In the case of high dimensional data such as images, it also refers to different features that can be extracted for a single-view such as a scene descriptor (GIST, [16]), Histogram of Oriented Gradients (HOG), and Local Binary Patterns (LBP) [17].…”

Section: Related Workmentioning

confidence: 99%

Multi-Perspective Anomaly Detection

Jakob

Madan

Schmid-Schirling

et al. 2021

Sensors

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Anomaly detection is a critical problem in the manufacturing industry. In many applications, images of objects to be analyzed are captured from multiple perspectives which can be exploited to improve the robustness of anomaly detection. In this work, we build upon the deep support vector data description algorithm and address multi-perspective anomaly detection using three different fusion techniques, i.e., early fusion, late fusion, and late fusion with multiple decoders. We employ different augmentation techniques with a denoising process to deal with scarce one-class data, which further improves the performance (ROC AUC =80%). Furthermore, we introduce the dices dataset, which consists of over 2000 grayscale images of falling dices from multiple perspectives, with 5% of the images containing rare anomalies (e.g., drill holes, sawing, or scratches). We evaluate our approach on the new dices dataset using images from two different perspectives and also benchmark on the standard MNIST dataset. Extensive experiments demonstrate that our proposed multi-perspective approach exceeds the state-of-the-art single-perspective anomaly detection on both the MNIST and dices datasets. To the best of our knowledge, this is the first work that focuses on addressing multi-perspective anomaly detection in images by jointly using different perspectives together with one single objective function for anomaly detection.

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Section: Related Workmentioning

confidence: 99%

Multi-Perspective Anomaly Detection

Jakob

Madan

Schmid-Schirling

et al. 2021

Sensors

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“…It focuses on three outlier types, including attributes, class, and class-attributes outliers. There is a need to accomplish such multi-view outlier detection because most existing approaches consider part of the problem [112]. Figure 13 illustrates the three different types of outliers.…”

Section: Spatiotemporal Outlier Detectionmentioning

confidence: 99%

Spatiotemporal Data Mining: A Survey on Challenges and Open Problems

Hamdi¹,

Shaban²,

Erradi³

et al. 2021

Preprint

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Spatiotemporal data mining (STDM) discovers useful patterns from the dynamic interplay between space and time. Several available surveys capture STDM advances and report a wealth of important progress in this field. However, STDM challenges and problems are not thoroughly discussed and presented in articles of their own. We attempt to fill this gap by providing a comprehensive literature survey on state-of-the-art advances in STDM. We describe the challenging issues and their causes and open gaps of multiple STDM directions and aspects. Specifically, we investigate the challenging issues in regards to spatiotemporal relationships, interdisciplinarity, discretisation, and data characteristics. Moreover, we discuss the limitations in the literature and open research problems related to spatiotemporal data representations, modelling and visualisation, and comprehensiveness of approaches. We explain issues related to STDM tasks of classification, clustering, hotspot detection, association and pattern mining, outlier detection, visualisation, visual analytics, and computer vision tasks. We also highlight STDM issues related to multiple applications including crime and public safety, traffic and transportation, earth and environment monitoring, epidemiology, social media, and Internet of Things.

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“…Perform P1M algorithm on Y to get η, U (t) , c (t) using ( 14) and (15), respectively; 4. Fix U (t) , c (t) , using (17) to get Wv of each view; 5. Fix Wv, using (12) to get Yv, and Yv of all views forms Y ; perform P1M algorithm on Y to get U (t+1) , c (t+1) using (15);…”

Section: 线性多视图原空间 -子空间 P1m (Lmo-sp1m)mentioning

confidence: 99%