Automatic Retrieval of Action Video Shots from the Web Using Density-Based Cluster Analysis and Outlier Detection

Nga Hang DO  Keiji YANAI  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E99-D   No.11   pp.2788-2795
Publication Date: 2016/11/01
Publicized: 2016/07/21
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2016EDP7108
Type of Manuscript: PAPER
Category: Image Processing and Video Processing
Keyword: 
automatic construction,  action datasets,  Web videos,  density-based clustering,  outlier detection,  

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Summary: 
In this paper, we introduce a fully automatic approach to construct action datasets from noisy Web video search results. The idea is based on combining cluster structure analysis and density-based outlier detection. For a specific action concept, first, we download its Web top search videos and segment them into video shots. We then organize these shots into subsets using density-based hierarchy clustering. For each set, we rank its shots by their outlier degrees which are determined as their isolatedness with respect to their surroundings. Finally, we collect high ranked shots as training data for the action concept. We demonstrate that with action models trained by our data, we can obtain promising precision rates in the task of action classification while offering the advantage of fully automatic, scalable learning. Experiment results on UCF11, a challenging action dataset, show the effectiveness of our method.