Multimedia Event Detection Using Segment-Based Approach for Motion Feature

Authors:
Sang Phan;Thanh Duc Ngo;Vu Lam;Son Tran;Duy-Dinh Le;Duc Anh Duong;Shin'ichi Satoh
Affiliations:
The Graduate University for Advanced Studies (SOKENDAI), Hayama, Japan 240-0193;The Graduate University for Advanced Studies (SOKENDAI), Hayama, Japan 240-0193;Faculty of Information Technology, Ho Chi Minh City University of Science, Ho Chi Minh City, Vietnam;Faculty of Information Technology, Ho Chi Minh City University of Science, Ho Chi Minh City, Vietnam;Multimedia Communications Lab, University of Information Technology, Ho Chi Minh City, Vietnam and National Institute of Informatics, Chiyoda-ku, Japan 101-8430;Multimedia Communications Lab, University of Information Technology, Ho Chi Minh City, Vietnam;National Institute of Informatics, Chiyoda-ku, Japan 101-8430
Venue:
Journal of Signal Processing Systems
Year:
2014

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Abstract

Multimedia event detection has become a popular research topic due to the explosive growth of video data. The motion features in a video are often used to detect events because an event may contain some specific actions or moving patterns. Raw motion features are extracted from the entire video first and then aggregated to form the final video representation. However, this video-based representation approach is ineffective when used for realistic videos because the video length can be very different and the clues for determining an event may happen in only a small segment of the entire video. In this paper, we propose using a segment-based approach for video representation. Basically, original videos are divided into segments for feature extraction and classification, while still keeping the evaluation at the video level. The experimental results on recent TRECVID Multimedia Event Detection datasets proved the effectiveness of our approach.