Semantic categorization of video: An evolutionary learning based approach
In this paper, we have proposed a fuzzy rule based system for classification of video into semantic categories. The classification scheme uses an evolutionary learning methodology to evolve a fuzzy system for use in the classification process. This evolved fuzzy classifier has the inherent capability to tackle variations and ambiguities invariably present in the video data. A novel fuzzy theoretic sheme has been suggested for extraction of key frames from a given video after shot segmentation. Frame based temporal features and spatial features obtained from key frames have been used in the classification system. We have developed an experimental system for categorization of sports video. The experimental system has yielded reasonably correct recognition results for a large number of samples.
Fast growth of search engines' popularity shows the users attraction to the Web engines. However there is a chance of misinterpretation for ambiguous queries. At this point, we propose a more adherence user interface which consist of a relevant visual content as well as generating new search snippet and title. Recent researches for meeting this aim are focused on a whole page thumbnail for assisting users to remember a recently visited Web page. Withal, this is not discussed yet that how a specific visual content of a page can allow users to distinguish between a useful and worthless page in the result page especially in an ambiguous search task. Our studya shows that the improvement in both textual search snippet and title as well as the additional thumbnail were helpful for users to clarify the Search Engine Result Page (SERP) in an ambiguous search task.