TRECVID 2010 Known-item Search by NUS

This paper describes our system for auto search and interactive search in the known-item search (KIS) task in TRECVID 2010. KIS task aims to find an unique video answer for each text query. The shift from traditional video search has prompted a series of challenges in processing and searching techniques that developed over the past few years. For the automatic search task, our VisionGo system performs query expansion and analysis, then employs multi-modality features including metadata, automatic speech recognition (ASR) and high level feature (HLF) to retrieve a ranked list of results deemed most relevant to the text-only query. To further improve the search performance, we crawl an extension set of tags from Youtube to supplement to TRECVID metadata. For interactive search task, we propose a new feedback scheme based on both related samples and exclusive negative samples to boost the search performance. To accomplish this, we introduce three enhancements to our VisioGo system: a) related sample feedback algorithm that allows users to indicate related (but not relevant) shots to the query; b) exclusive negative sample selection approach; and c) clustered shot-icons for efficiently representing the whole content of the video. Results from TRECVID 2010 video test set indicate that the enhancements are effective.