Elements of information theory
Elements of information theory
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Video search reranking via information bottleneck principle
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
A support vector method for optimizing average precision
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
A reranking approach for context-based concept fusion in video indexing and retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Video search reranking through random walk over document-level context graph
Proceedings of the 15th international conference on Multimedia
Multimedia search with pseudo-relevance feedback
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Interactive video search using multilevel indexing
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
Adaptive wavelet thresholding for image denoising and compression
IEEE Transactions on Image Processing
CrowdReranking: exploring multiple search engines for visual search reranking
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Topic prerogative feature selection using multiple query examples for automatic video retrieval
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Multigraph-based query-independent learning for video search
IEEE Transactions on Circuits and Systems for Video Technology
Co-reranking by mutual reinforcement for image search
Proceedings of the ACM International Conference on Image and Video Retrieval
Visual search reranking via adaptive particle swarm optimization
Pattern Recognition
Joint-rerank: a novel method for image search reranking
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
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This paper is concerned with video search reranking - the task of reordering the initial ranked documents (video shots) to improve the search performance - in an optimization framework. Conventional supervised reranking approaches empirically convert the reranking as a classification problem in which each document is determined relevant or not, followed by reordering the documents according to the confidence scores of classification. We argue that reranking is essentially an optimization problem in which the ranked list is globally optimal if any two arbitrary documents from the list are correctly ranked in terms of relevance, rather than simply classifying a document into relevant or not. Therefore, we propose in this paper to directly optimize video search reranking from a novel viewpoint of information theory, that is, to identify an optimal set of correctly-ranked document pairs which maximally preserves the relevant information and simultaneously carries the irrelevant information as little as possible. The final reranked list is then directly recovered from this optimal set of pairs. Under the framework, we further propose an effective algorithm, called minimum incremental information loss (MIIL) reranking, to solve the optimization problem more practically. We conducted comprehensive experiments on automatic video search task over TRECVID 2005-2007 benchmarks, and showed significant and consistent improvements over the text search baseline and other reranking approaches.