Detecting anomalous regions from an image based on deep captioning

Yusuke Hatae, Qingpu Yang, Muhammad Fikko Fadjrimiratno, Yuanyuan Li, Tetsu Matsukawa, Einoshin Suzuki

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

2 被引用数 (Scopus)

抄録

In this paper we propose a one-class anomalous region detection method from an image based on deep captioning. Such a method can be installed on an autonomous mobile robot, which reports anomalies from observation without any human supervision and would interest a wide range of researchers, practitioners, and users. In addition to image features, which were used by conventional methods, our method exploits recent advances in deep captioning, which is based on deep neural networks trained on a large-scale data on image - caption pairs, enabling anomaly detection in the semantic level. Incremental clustering is adopted so that the robot is able to model its observation into a set of clusters and report substantially new observations as anomalies. Extensive experiments using two real-world data demonstrate the superiority of our method in terms of recall, precision, F measure, and AUC over the traditional approach. The experiments also show that our method exhibits excellent learning curve and low threshold dependency.

本文言語英語
ホスト出版物のタイトルVISAPP
編集者Giovanni Maria Farinella, Petia Radeva, Jose Braz
出版社SciTePress
ページ326-335
ページ数10
ISBN(電子版)9789897584022
出版ステータス出版済み - 2020
イベント15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2020 - Valletta, マルタ
継続期間: 2 27 20202 29 2020

出版物シリーズ

名前VISIGRAPP 2020 - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
5

会議

会議15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2020
Countryマルタ
CityValletta
Period2/27/202/29/20

All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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