EFFECTIVE AND EFFICIENT QUERY PROCESSING FOR VIDEO SUBSEQUENCE IDENTIFICATION



ABSTRACT

This paper presents a graph transformation and matching approach to identify the occurrence of potentially different ordering or length due to content editing. With a novel batch query algorithm to retrieve similar frames, the mapping relationship between the query and database video is first represented by a bipartite graph. The densely matched parts along the long sequence are then extracted, followed by a filter-and-refine search strategy to prune some irrelevant subsequences. During the filtering stage, Maximum Size Matching is deployed for each sub graph constructed by the query and candidate subsequence to obtain a smaller set of candidates. During the refinement stage, Sub-Maximum Similarity Matching is devised to identify the subsequence with the highest aggregate score from all candidates, according to a robust video similarity model that incorporates visual content, temporal order, and frame alignment information