Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIAM Journal on Optimization
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
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
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
VisualRank: Applying PageRank to Large-Scale Image Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust sparse rank learning for non-smooth ranking measures
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
CrowdReranking: exploring multiple search engines for visual search reranking
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Multimedia search with pseudo-relevance feedback
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Latent visual context analysis for image re-ranking
Proceedings of the ACM International Conference on Image and Video Retrieval
Supervised reranking for web image search
Proceedings of the international conference on Multimedia
Learning to re-rank: query-dependent image re-ranking using click data
Proceedings of the 20th international conference on World wide web
Typicality-Based Visual Search Reranking
IEEE Transactions on Circuits and Systems for Video Technology
Harvesting visual concepts for image search with complex queries
Proceedings of the 20th ACM international conference on Multimedia
Time-sensitive web image ranking and retrieval via dynamic multi-task regression
Proceedings of the sixth ACM international conference on Web search and data mining
Anchor concept graph distance for web image re-ranking
Proceedings of the 21st ACM international conference on Multimedia
Query-dependent visual dictionary adaptation for image reranking
Proceedings of the 21st ACM international conference on Multimedia
Search-based relevance association with auxiliary contextual cues
Proceedings of the 21st ACM international conference on Multimedia
Multimedia search reranking: A literature survey
ACM Computing Surveys (CSUR)
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Visual reranking has become a widely-accepted method to improve traditional text-based image search engines. Its basic principle is that visually similar images should have similar ranking scores. While existing methods are different in specifics, almost all of them are based on explicit or implicit pseudo-relevance feedback (PRF). Explicit PRF-based approaches, including classification-based and clustering-based reranking, suffer from the difficulty of selecting reliable positive and negative samples. Implicit PRF-based approaches, such as graph-based and Bayesian visual reranking, deal with such unreliability by making use of the initial ranking in a soft manner, but have limited capability of promoting relevant images and lowering down irrelevant images. In this paper, we propose l1 square loss optimization based on sparsity and ranking constraints to detect confident samples which are most likely to be relevant to a query. Based on the discovered confident samples, we present an adaptive kernel-based scheme to rerank the images. The success of our proposed method comes from another important observation that irrelevant images, whether initially positioned at the top or bottom, are usually less-popular and more diverse than relevant images. Therefore, it is robust against outlier images and suitable when relevant images are multi-modally distributed. The experimental results demonstrate significant improvement of our method over several existing reranking approaches on both MSRA-MM V1.0 and Web Queries datasets.