SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
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
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Predicting query difficulty on the web by learning visual clues
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
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
Supervised Learning of Semantic Classes for Image Annotation and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improved query difficulty prediction for the web
Proceedings of the 17th ACM conference on Information and knowledge management
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
MM '09 Proceedings of the 17th ACM international conference on Multimedia
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
What does classifying more than 10,000 image categories tell us?
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Co-occurrence based predictors for estimating query difficulty
ICDMW '10 Proceedings of the 2010 IEEE International Conference on Data Mining Workshops
Query difficulty prediction for contextual image retrieval
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
A bag-of-objects retrieval model for web image search
Proceedings of the 20th ACM international conference on Multimedia
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Given the explosive growth of the Web and the popularity of image sharing Web sites, image retrieval plays an increasingly important role in our daily lives. Search engines aim to provide beneficial image search results to users in response to queries. The quality of image search results depends on many factors: chosen search algorithms, ranking functions, indexing features, the base image database, etc. Applying different settings for these factors generates search result lists with varying levels of quality. Previous research has shown that no setting can always perform optimally for all queries. Therefore, given a set of search result lists generated by different settings, it is crucial to automatically determine which result list is the best in order to present it to users. This paper proposes a novel method to automatically identify the best search result list from a number of candidates. There are three main innovations in this paper. First, we propose a preference learning model to quantitatively study the best image search result identification problem. Second, we propose a set of valuable preference learning related features by exploring the visual characters of returned images. Third, our method shows promising potential in applications such as reranking ability assessment and optimal search engine selection. Experiments on two image search datasets show that our method achieves about 80% prediction accuracy for reranking ability assessment, and selects optimal search engine for about 70% queries correctly.