Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Modern Information Retrieval
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Image and Feature Co-Clustering
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Web image clustering by consistent utilization of visual features and surrounding texts
Proceedings of the 13th annual ACM international conference on Multimedia
Content-based image retrieval: approaches and trends of the new age
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
ImprovingWeb-based Image Search via Content Based Clustering
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
VisualRank: Applying PageRank to Large-Scale Image Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mining the web for visual concepts
Proceedings of the 9th International Workshop on Multimedia Data Mining: held in conjunction with the ACM SIGKDD 2008
Visual diversification of image search results
Proceedings of the 18th international conference on World wide web
Web image retrieval reranking with multi-view clustering
Proceedings of the 18th international conference on World wide web
Lightweight web image reranking
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Jointly optimising relevance and diversity in image retrieval
Proceedings of the ACM International Conference on Image and Video Retrieval
Actions in stillweb images: visualization, detection and retrieval
WAIM'11 Proceedings of the 12th international conference on Web-age information management
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General image retrieval systems exploit text and link structure to "understand" the content of the web images and lack the discriminative power to deliver visually diverse search results. The result list often contains hundreds of pages, most of which may not be visited, costing a lot of time and energy of users. Unfortunately, many high quality images, containing more visual and semantic information, may appear at these back pages. To tackle this problem, we introduce a re-ranking method called Dual-Rank to improve web image retrieval by clustering and reordering the images retrieved from an image search engine. We first utilize multipartite graph model to represent images and features, then formulate clustering as a constrained multi-objective optimization problem, which can be efficiently solved by semi-definite programming (SDP). The framework of Dual-Rank is composed of Inter-cluster Rank and Intra-cluster Rank, and could rank clusters and images respectively. Our method is evaluated against a standard search engine and significant improvements are reported in terms of MAP, D@n and user experience.