Generic Video Classification: An Evolutionary Learning Based Fuzzy Theoretic Approach
نویسندگان
چکیده
In this paper we propose an evolutionary learning based fuzzy theoretic approach for classifying video sequences into generic categories. This categorization is based on video structure based syntactic features. The features like shot durations, editing style, camera work and shot activity conveys large amount of information about the type of video. The information derived from these features is integrated over a larger time-scale than a shot length time to form fuzzy rules for the extraction of video structure based features. Evolutionary learning paradigm is used to evolve the fuzzy rule based system for generic video characterization. Such a rule-based system yields high representational accuracy of the classes as shown by the experiments conducted on various type of video sequences ranging up to 1 to 3 minutes. Experimental analysis illustrates the effectiveness of our system in offering a novel approach for categorizing the video sequences.
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