TRECVID 2007 Search Tasks by NUS-ICT
Tat‐Seng Chua, Shi-Yong Neo, Yan-Tao Zheng, Hai-Kiat Goh, Xiaoming Zhang, Sheng Tang, Yongdong Zhang, Jintao Li, Juan Cao, Huanbo Luan, Qiaoyan He, Xu Zhang · 2007
This paper describes the details of our systems for our automated and interactive search in TRECVID 2007. The shift from news video to documentary video this year has prompted a series of changes in processing techniques from that developed over the past few years. For the automated search task, we employ our previous querydependent retrieval which automatically discovers query class and query-high-level-features (query-HLF) to fuse available multimodal features. Different from previous works, our system this year gives more emphasis to visual features such as color, texture and motion in the video source. The reasons are: (a) given the low quality of ASR text and the more visual and motion oriented queries, we expect the visual features to be as discriminating as text feature; and (b) the appropriate use of motion features is highly effective for queries as they are able to model intra-frame changes. For the interactive task, we first utilize the results from the automated search results for user feedback. The user is able to make use of our intuitive retrieval interface with a variety of relevance feedback techniques to refine the search results. In addition, we introduce the motion-icons, which allow users to see a dynamic series of keyframes instead of a single keyframe during assessment. Results show that the approach can help in providing better discrimination. 1.