Making large-scale support vector machine learning practical
Advances in kernel methods
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
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
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Learning Object Categories from Google"s Image Search
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Video search reranking through random walk over document-level context graph
Proceedings of the 15th international conference on Multimedia
Introduction to Information Retrieval
Introduction to Information Retrieval
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
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
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
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
A bag-of-objects retrieval model for web image search
Proceedings of the 20th ACM international conference on Multimedia
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Visual reranking aims at improving the precision of text-based Web image search. In this paper we propose to combine two learning strategies for deriving the reranking model: learning from search engine and learning from human supervision. The first strategy learns the reranking model in a pseudo-supervised fashion by interpreting parts of the initial text-based search result as pseudo-relevant. The second strategy involves manual relevance labeling of the text-based search results obtained for a limited number of representative queries. While learning from search engine is query dependent and can therefore adapt better to individual queries, it is essentially unsupervised and noisy. While human supervision can better relate the search results to true relevance criteria, it needs to be deployed in a way to keep the reranking scalable. A combination of the two is expected to benefit from their respective advantages and reduce the impact of their individual deficiencies. We propose a two-stage learning approach to visual reranking, where in the online stage multiple query-relative meta rerankers are learned in a pseudo-supervised fashion from the search results and in the offline stage human supervision is used to derive the final reranking function based on these meta rerankers. The experimental results demonstrate that the proposed method significantly outperforms the existing reranking approaches.