Object Mining for Large Video data

Abstract

We propose a method for achieving a novel concise graph-based representation for retrieval of objects from large video data. The emphasis in this paper is towards achieving a compact representation of video data for faster retrieval. Specifically, we use information available from scripts and subtitles in order to group all occurrences of an object in video data, which provides a separate representation for each scene. Further, based on the premise that the number of objects in a shot are typically much less than the number of video frames in that shot, we propose a graph-based representation in which vertices represent objects rather than video frames. Key advantages of the proposed approach include faster retrieval, efficiency in performing tasks such as spatial re-ranking and graph partitioning and a single representation for both retrieval and summarization applications. We demonstrate efficacy of the proposed approach in retrieval and summarization applications over video data consisting of episodes of a popular TV series "Friends".