Abstract
Many researchers try to utilize the semantic information extracted from visual feature to directly realize the semantic video retrieval or to supplement the automated speech recognition (ASR) text retrieval. But bridging the gap between the low-level visual feature and semantic content is still a challenging task. In this paper, we study how to effectively use Latent Semantic Indexing (LSI) to improve the semantic video retrieval through the ASR texts. The basic LSI method has been shown effective in the traditional text retrieval and the noisy ASR text retrieval. In this paper, we further use the lexicon-guided semantic clustering to effectively remove the noise introduced by news video’s additional contents, and use the cluster-based LSI to automatically mine the semantic structure underlying the terms expression. Tests on the TRECVID 2005 dataset show that the above two enhancements achieve 21.3% and 6.9% improvements in performance over the traditional vector-space model(VSM) and the basic LSI separately.
This paper is supported by National Basic Research Program of China (973 Program, 2007CB311100), Beijing Science and Technology Planning Program of China (D01060-08040291) and National High Technology and Research Development Program of China (863 Program, 2007AA01Z416).
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Cao, J., Tang, S., Li, J., Zhang, Y., Pan, X. (2007). A Lexicon-Guided LSI Method for Semantic News Video Retrieval. In: Ip, H.HS., Au, O.C., Leung, H., Sun, MT., Ma, WY., Hu, SM. (eds) Advances in Multimedia Information Processing – PCM 2007. PCM 2007. Lecture Notes in Computer Science, vol 4810. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77255-2_21
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DOI: https://doi.org/10.1007/978-3-540-77255-2_21
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