A novel method for spoken text feature extraction in semantic video retrieval

Authors:
Juan Cao;Jintao Li;Yongdong Zhang;Sheng Tang
Affiliations:
Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
Venue:
PCM'06 Proceedings of the 7th Pacific Rim conference on Advances in Multimedia Information Processing
Year:
2006

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Abstract

We propose a novel method for extracting text feature from the automatic speech recognition (ASR) results in semantic video retrieval. We combine HowNet-rule-based knowledge with statistic information to build special concept lexicons, which can rapidly narrow the vocabulary and improve the retrieval precision. Furthermore, we use the term precision (TP) weighting method to analyze ASR texts. This weighting method is sensitive to the sparse but important terms in the relevant documents. Experiments show that the proposed method is effective for semantic video retrieval.