Non-sequential multiscale content-based video decomposition

Quality criteria are introduced to evaluate the efficiency of the proposed scheme. The efficiency of the organization is maximized if multiscale content decomposition is performed using content representatives and constructing content classes. Content representatives are estimated in our approach as the ones of the maximum dissimilarity, expressed by a distance metric. The optimization is conducted by incorporating a stochastic algorithm of logarithmically reduced searching area (stochastic logarithmic). Experimental results on real-life video sequences show that the proposed multiscale video organization enables users to detect content of interest much faster, compared to the conventional sequential video scanning or other video decomposition/summarization methods, resulting in a better organization efficiency as measured by the quality criteria.