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Image search reranking is an effective approach to refining the text-based image search result. In the reranking process, the estimation of visual similarity is critical to the performance. However, the existing measures, based on global or local features, cannot be adapted to different queries. In this paper, we propose to estimate a query aware image(More)
—Visual reranking has been proven effective to refine text-based video and image search results. It utilizes visual information to recover " true " ranking list from the noisy one generated by text-based search. Visual reranking improves text-based search results by incorporating both textual and visual information. In this paper, we model the textual and(More)
V ideo copy-detection, the purpose of which is to find a video copy in a repository, is important for many applications. 1-3 Our research focuses on locating video clips that are copied but maintain frame correspondence. Although this issue has been investigated for several years in multimedia research, efficient and scalable copy-detection is still a(More)
One of the fundamental problems in image search is to rank image documents according to a given textual query. We address two limitations of the existing image search engines in this paper. First, there is no straightforward way of comparing textual keywords with visual image content. Image search engines therefore highly depend on the surrounding texts,(More)
Content-based video search reranking can be regarded as a process that uses visual content to recover the "true" ranking list from the noisy one generated based on textual information. This paper explicitly formulates this problem in the Bayesian framework, i.e., maximizing the ranking score consistency among visually similar video shots while minimizing(More)
Visual reranking is effective to improve the performance of the text-based video search. However, existing reranking algorithms can only achieve limited improvement because of the well-known semantic gap between low-level visual features and high-level semantic concepts. In this article, we adopt interactive video search reranking to bridge the semantic gap(More)
Image search reranking methods usually fail to capture the user's intention when the query term is ambiguous. Therefore, reranking with user interactions, or active reranking, is highly demanded to effectively improve the search performance. The essential problem in active reranking is how to target the user's intention. To complete this goal, this paper(More)
Vectored data frequently occur in a variety of fields, which are easy to handle since they can be mathematically abstracted as points residing in a Euclidean space. An appropriate distance metric in the data space is quite demanding for a great number of applications. In this paper, we pose robust and tractable metric learning under pairwise constraints(More)
This paper describes the MSRA experiments for TRECVID 2008. We performed the experiments in high-level feature extraction and automatic search tasks. For high-level feature extraction , we representatively investigated the benefit of global and local low-level features by a variety of learning-based methods, including supervised and semi-supervised learning(More)