Modern Information Retrieval
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
The Journal of Machine Learning Research
Web Image Retrieval Re-Ranking with Relevance Model
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
Learning Object Categories from Google"s Image Search
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Joint visual-text modeling for automatic retrieval of multimedia documents
Proceedings of the 13th annual ACM international conference on Multimedia
Automatic discovery of query-class-dependent models for multimodal search
Proceedings of the 13th annual ACM international conference on Multimedia
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Video search reranking via information bottleneck principle
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Learning to rank: from pairwise approach to listwise approach
Proceedings of the 24th international conference on Machine learning
Video search re-ranking via multi-graph propagation
Proceedings of the 15th international conference on Multimedia
Video search reranking through random walk over document-level context graph
Proceedings of the 15th international conference on Multimedia
Semantic concept-based query expansion and re-ranking for multimedia retrieval
Proceedings of the 15th international conference on Multimedia
Video suggestion and discovery for youtube: taking random walks through the view graph
Proceedings of the 17th international conference on World Wide Web
Correlative multilabel video annotation with temporal kernels
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
VisualRank: Applying PageRank to Large-Scale Image Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian video search reranking
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Real time google and live image search re-ranking
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Optimizing video search reranking via minimum incremental information loss
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
A generalized Co-HITS algorithm and its application to bipartite graphs
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
OPTIMOL: Automatic Online Picture Collection via Incremental Model Learning
International Journal of Computer Vision
Multimedia search with pseudo-relevance feedback
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Active reranking for web image search
IEEE Transactions on Image Processing
Co-reranking by mutual reinforcement for image search
Proceedings of the ACM International Conference on Image and Video Retrieval
Supervised reranking for web image search
Proceedings of the international conference on Multimedia
Visual reranking with local learning consistency
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
Bridging the Semantic Gap Between Image Contents and Tags
IEEE Transactions on Multimedia
A heterogenous automatic feedback semi-supervised method for image reranking
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Image search reranking which aims to improve the text-based image search results with the help of other cues has grown into a hot research topic. Most existing reranking methods only focus on image visual cues. However, the visual cues cannot always guarantee to provide enough information for the reranking process. Thus, some approaches try to fuse multiple image cues for reranking. These methods do not or weakly exploit the relationships among multiple image cues. In this paper, we present a novel image reranking framework---Joint-Rerank, which considers image multiple modalities (or multiple cues) jointly as interdependent attributes of an image entity. Joint-Rerank models the images as a multigraph where each image is a node with multimodal attributes (textual and visual cues) and the parallel edges between nodes measure both image intra-modal and inter-modal similarities. Besides, each node has a 'self-consistency' that measures how much the multiple modalities of an image may be consistent. To solve the reranking problem, we first degenerate the multigraph into a new complete graph, and then employ a random walk on the degenerated graph to propagate the relevance scores of each node. Finally, the relevance scores of multiple modalities are fused to rank the images. Moreover, in Joint-Rerank, cross-modal walk is possible, i.e., a surfer can jump from one image to another following both intra-modal and inter-modal links. Experimental results on a large web queries dataset which contains 353 image search queries show that Joint-Rerank is superior or highly competitive to several state-of-the-art reranking algorithms.