| dc.description.abstract | With the Proliferation of multimedia data, requests for effective and efficient video retrieval are growing. Among the various kinds of digital videos, TV news videos play an important role in broadcasting nowadays and may also serve as a major source of daily information for people these days. In Taiwan, there are several TV news stations and duplicated news videos are repeated again and again. Watching them may be a waste of time. Considering that the digital recording facilities are widely available
now, we propose a classification scheme that can cluster the recorded TV news video segments so that the viewers may choose to watch the related archived news and even retrieve the useful information from them.
In the proposed scheme, we make use of the text in TV news for clustering videos. It should be noted that the text analysis in Taiwan’s TV news needs further processing since the text areas in Taiwan’s TV news may include various information including the caption, weather report, and stock market indices etc. It’s challenging to locate the area where we are really interested in. Furthermore, video OCR is not mature enough and does not work quite well in Taiwan’s TV news broadcasting because of the special and different text fonts used in each TV news channel. We apply the low-level feature extraction and SVM to locate the possible region of interest, which should help to differentiate new segments from commercials. Then the anchorperson scene will be located to divide a piece of news into two parts, one part with the anchorperson describing the news and the other part related to the news content itself. Next, we extract the caption in the second part, in which the text is more stable and representative. After refining the extracted text areas, a cross-correlation process is used to find the similar pattern in captions of video segments to relate them together. Experimental results will be
shown to demonstrate the feasibility of this potential solution.
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