Co-retrieval: A Boosted Reranking Approach for Video Retrieval

Video retrieval compares multimedia queries to a video collection in multiple dimensions and combines all the retrieval scores into a nal ranking. Although text are the most reliable feature for video retrieval, features from other modalities can provide complementary information. This paper presents a reranking framework for video retrieval to augment retrieval based on text features with other evidence. We also propose a boosted reranking algorithm called co-retrieval, which combines a boosting type algorithm and a noisy label prediction scheme to automatically select the most useful weak hypotheses for di erent queries. The proposed approach is evaluated with queries and video from the 65-hour test collection of the 2003 NIST TRECVID evaluation.