Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Influence sets based on reverse nearest neighbor queries
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
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
Modern Information Retrieval
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Video search reranking via information bottleneck principle
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
The challenge problem for automated detection of 101 semantic concepts in multimedia
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
How many high-level concepts will fill the semantic gap in news video 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
Predicting diverse subsets using structural SVMs
Proceedings of the 25th international conference on Machine learning
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
Visual diversification of image search results
Proceedings of the 18th international conference on World wide web
Foundations and Trends in Information Retrieval
Multimedia search with pseudo-relevance feedback
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Visual reranking with local learning consistency
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
Learning from search engine and human supervision for web image search
MM '11 Proceedings of the 19th ACM international conference on Multimedia
The role of attractiveness in web image search
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Learning to judge image search results
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
Oracle in Image Search: A Content-Based Approach to Performance Prediction
ACM Transactions on Information Systems (TOIS)
Leveraging user comments for aesthetic aware image search reranking
Proceedings of the 21st international conference on World Wide Web
A unified context model for web image retrieval
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Ordinal preserving projection: a novel dimensionality reduction method for image ranking
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Joint-rerank: a novel method for image search reranking
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
A bag-of-objects retrieval model for web image search
Proceedings of the 20th ACM international conference on Multimedia
Proceedings of the 20th ACM international conference on Multimedia
Attribute-assisted reranking for web image retrieval
Proceedings of the 20th ACM international conference on Multimedia
Intent and its discontents: the user at the wheel of the online video search engine
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
Leveraging exemplar and saliency model for image search reranking
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
Ordinal regularized manifold feature extraction for image ranking
Signal Processing
A novel representative image selection method in lager-scale image dataset
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
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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Visual search reranking that aims to improve the text-based image search with the help from visual content analysis has rapidly grown into a hot research topic. The interestingness of the topic stems mainly from the fact that the search reranking is an unsupervised process and therefore has the potential to scale better than its main alternative, namely the search based on offline-learned semantic concepts. However, the unsupervised nature of the reranking paradigm also makes it suffer from problems, the main of which can be identified as the difficulty to optimally determine the role of visual modality over different application scenarios. Inspired by the success of the "learning-to-rank" idea proposed in the field of information retrieval, we propose in this paper the "learning-to-rerank" paradigm, which derives the reranking function in a supervised fashion from the human-labeled training data. Although supervised learning is introduced, our approach does not suffer from scalability issues since a unified reranking model is learned that can be applied to all queries. In other words, a query-independent reranking model will be learned for all queries using query-dependent reranking features. The query-dependent reranking feature extraction is challenging since the textual query and the visual documents have different representation. In this paper, 11 lightweight reranking features are proposed by representing the textual query using visual context and pseudo relevant images from the initial search result. The experiments performed on two representative Web image datasets demonstrate that the proposed learning-to-rerank algorithm outperforms the state-of-the-art unsupervised reranking methods, which makes the learning-to-rerank paradigm a promising alternative for robust and reliable Web-scale image search.