The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
IR evaluation methods for retrieving highly relevant documents
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Object Recognition with Informative Features and Linear Classification
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Learning Object Categories from Google"s Image Search
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Content-based multimedia information retrieval: State of the art and challenges
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
International Journal of Computer Vision
Video search reranking via information bottleneck principle
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
A reranking approach for context-based concept fusion in video indexing and retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
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
Pagerank for product image search
Proceedings of the 17th international conference on World Wide Web
Bayesian video search reranking
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
Multimedia search with pseudo-relevance feedback
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Co-reranking by mutual reinforcement for image search
Proceedings of the ACM International Conference on Image and Video Retrieval
Weighting visual features with pseudo relevance feedback for CBIR
Proceedings of the ACM International Conference on Image and Video Retrieval
Visual query suggestion: Towards capturing user intent in internet image search
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
The third eye: mining the visual cognition across multi-language communities
Proceedings of the international conference on Multimedia
Boosting-based multiple kernel learning for image re-ranking
Proceedings of the international conference on Multimedia
Connecting with the collective: self-contained reranking for collaborative recommendation
Proceedings of the 1st ACM international workshop on Connected multimedia
Automatic image annotation by using relevant keywords extracted from auxiliary text documents
Proceedings of the international workshop on Very-large-scale multimedia corpus, mining and retrieval
Integrating bilingual searches for junk image filtering
ICIMCS '10 Proceedings of the Second International Conference on Internet Multimedia Computing and Service
Contextual Video Recommendation by Multimodal Relevance and User Feedback
ACM Transactions on Information Systems (TOIS)
Visual search reranking via adaptive particle swarm optimization
Pattern Recognition
JIGSAW: interactive mobile visual search with multimodal queries
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Robust visual reranking via sparsity and ranking constraints
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Collaborative video reindexing via matrix factorization
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Similarity query postprocessing by ranking
AMR'10 Proceedings of the 8th international conference on Adaptive Multimedia Retrieval: context, exploration, and fusion
Cross community news event summary generation based on collaborative ranking
Proceedings of the 4th International Conference on Internet Multimedia Computing and Service
Find you wherever you are: geographic location and environment context-based pedestrian detection
Proceedings of the ACM multimedia 2012 workshop on Geotagging and its applications in multimedia
Search web images using objects, backgrounds and conditions
Proceedings of the 20th ACM international conference on Multimedia
Multimodal re-ranking of product image search results
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Near-lossless semantic video summarization and its applications to video analysis
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Click-boosting random walk for image search reranking
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
Memory recall based video search: Finding videos you have seen before based on your memory
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Multimedia search reranking: A literature survey
ACM Computing Surveys (CSUR)
Hi-index | 0.00 |
Most existing approaches to visual search reranking predominantly focus on mining information within the initial search results. However, the initial ranked list cannot provide enough cues for reranking by itself due to the typically unsatisfying visual search performance. This paper presents a new method for visual search reranking called CrowdReranking, which is characterized by mining relevant visual patterns from image search results of multiple search engines which are available on the Internet. Observing that different search engines might have different data sources for indexing and methods for ranking, it is reasonable to assume that there exist different search results yet certain common visual patterns relevant to a given query among those results. We first construct a set of visual words based on the local image patches collected from multiple image search engines. We then explicitly detect two kinds of visual patterns, i.e., salient and concurrent patterns, among the visual words. Theoretically, we formalize reranking as an optimization problem on the basis of the mined visual patterns and propose a close-form solution. Empirically, we conduct extensive experiments on several real-world search engines and one benchmark dataset, and show that the proposed CrowdReranking is superior to the state-of-the-art works.