Active Bucket Categorization for High Recall Video Retrieval
There are large amounts of digital video available. High recall retrieval of these requires going beyond the ranked results, which is the common target in high precision retrieval. To aid high recall retrieval, we propose Active Bucket Categorization, which is a multicategory interactive learning strategy which extends MediaTable [1], our multimedia categorization tool. MediaTable allows users to place video shots into buckets: user-assigned subsets of the collection. Our Active Bucket Categorization approach augments this by unobtrusively expanding these buckets with related footage from the whole collection. In this paper, we propose an architecture for active bucket-based video retrieval, evaluate two different learning strategies, and show its use in video retrieval with an evaluation using three groups of nonexpert users. One baseline group uses only the categorization features of MediaTable such as sorting and filtering on concepts and fast grid preview, but no online learning mechanisms. One group uses on-demand passive buckets. The last group uses fully automatic active buckets which autonomously add content to buckets. Results indicate a significant increase in the number of relevant items found for the two groups of users using bucket expansions, yielding the best results with fully automatic bucket expansions, thereby aiding high recall video retrieval significantly.
video retrieval active learning interactive video retrieval multi class categorization relevance feedback user evaluation