Probability Estimates for Multi-class Classification by Pairwise Coupling
The Journal of Machine Learning Research
Learning Object Categories from Google"s Image Search
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
A web-based kernel function for measuring the similarity of short text snippets
Proceedings of the 15th international conference on World Wide Web
Video search reranking via information bottleneck principle
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Video search reranking through random walk over document-level context graph
Proceedings of the 15th international conference on Multimedia
Introduction to Information Retrieval
Introduction to Information Retrieval
VisualRank: Applying PageRank to Large-Scale Image Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multimedia search with pseudo-relevance feedback
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Robust visual reranking via sparsity and ranking constraints
MM '11 Proceedings of the 19th ACM international conference on Multimedia
IntentSearch: Capturing User Intention for One-Click Internet Image Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Multimedia
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Web image re-ranking aims to automatically refine the initial text-based image search results by employing visual information. A strong line of work in image re-ranking relies on building image graphs that requires computing distances between image pairs. In this paper, we present Anchor Concept Graph Distance (ACG Distance), a novel distance measure for image re-ranking. For a given textual query, an Anchor Concept Graph (ACG) is automatically learned from the initial text-based search results. The nodes of the ACG (i.e., anchor concepts) and their correlations well model the semantic structure of the images to be re-ranked. Images are projected to the anchor concepts. The projection vectors undergo a diffusion process over the ACG, and then are used to compute the ACG distance. The ACG distance reduces the semantic gap and better represents distances between images. Experiments on the MSRA-MM and INRIA datasets show that the ACG distance consistently outperforms existing distance measures and significantly improves start-of-the-art methods in image re-ranking.