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Computer Vision, Imaging and Computer Graphics Theory and Applications : 14th International Joint Conference, VISIGRAPP 2019, Prague, Czech Republic, February 25–27, 2019, Revised Selected Papers
- 2020
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Samlingsverk (redaktörskap) (refereegranskat)abstract
- This book constitutes thoroughly revised and selected papers from the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2019, held in Prague, Czech Republic, in February 2019. The 25 thoroughly revised and extended papers presented in this volume were carefully reviewed and selected from 395 submissions. The papers contribute to the understanding of relevant trends of current research on computer graphics; human computer interaction; information visualization; computer vision.
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Computer Vision, Imaging and Computer Graphics Theory and Applications : 15th International Joint Conference, VISIGRAPP 2020 Valletta, Malta, February 27–29, 2020, Revised Selected Papers
- 2022. - 1
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Samlingsverk (redaktörskap) (refereegranskat)abstract
- This book constitutes thoroughly revised and selected papers from the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2020, held in Valletta, Malta, in February 2020. The 25 thoroughly revised and extended papers presented in this volume were carefully reviewed and selected from 455 submissions. The papers contribute to the understanding of relevant trends of current research on computer graphics; human computer interaction; information visualization; computer vision.
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- Cheng, Lingyun, et al.
(författare)
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Interactive Anomaly Detection Based on Clustering and Online Mirror Descent
- 2020
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Konferensbidrag (refereegranskat)abstract
- In several applications, when anomalies are detected, human experts have to investigate or verify them one by one. As they investigate, they unwittingly produce a label - true positive (TP) or false positive (FP). In this paper, we propose a method (called OMD-Clustering) that exploits this label feedback to minimize the FP rate and detect more relevant anomalies, while minimizing the expert effort required to inves- tigate them. The OMD-Clustering method iteratively suggests the top-1 anomalous instance to a human expert and receives feedback. Before suggesting the next anomaly, the method re-ranks instances so that the top anomalous instances are similar to the TP instances and dissimi- lar to the FP instances. This is achieved by learning to score anomalies differently in various regions of the feature space. An experimental eval- uation on several real-world datasets is conducted. The results show that OMD-Clustering achieves significant improvement in both detection pre- cision and expert effort compared to state-of-the-art interactive anomaly detection methods.
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