Combining supervised learning with color correlograms for content-based image retrieval
MULTIMEDIA '97 Proceedings of the fifth ACM international conference on Multimedia
Performance evaluation in content-based image retrieval: overview and proposals
Pattern Recognition Letters - Special issue on image/video indexing and retrieval
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modern Information Retrieval
Benchmarking Multimedia Databases
Multimedia Tools and Applications
Benchmarking for Content-Based Visual Information Search
VISUAL '00 Proceedings of the 4th International Conference on Advances in Visual Information Systems
Combining multi-visual features for efficient indexing in a large image database
The VLDB Journal — The International Journal on Very Large Data Bases
NeTra: a toolbox for navigating large image databases
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 1 - Volume 1
The Hybrid Tree: An Index Structure for High Dimensional Feature Spaces
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
CMVF: a novel dimension reduction scheme for efficient indexing in a large image database
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
WALRUS: A Similarity Retrieval Algorithm for Image Databases
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Generic Scheme for Color Image Retrieval Based on the Multivariate Wald-Wolfowitz Test
IEEE Transactions on Knowledge and Data Engineering
Evaluation axes for medical image retrieval systems: the imageCLEF experience
Proceedings of the 13th annual ACM international conference on Multimedia
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While content-based image retrieval (CBIR) is an expanding field, and new approaches to ever more effective retrieval are frequently proposed, relatively little attention has so far been paid to the process of evaluating the effectiveness of CBIR methods. Most of the reported evaluations use standard IR evaluation methodologies, with little consideration of their statistical significance or appropriateness for CBIR, which makes it difficult to assess the precise impact of individual methods. In this paper, we present a new approach for evaluating CBIR systems which provides both efficient and statistically-sound performance evaluation. The approach is based on stratified sampling, and provides a significant improvement over existing evaluation approaches. Comprehensive experiments using our approach to evaluate a range of CBIR methods have shown that the approach reduces not only the estimation error, but also reduces the size of the test data set required to achieve specific estimation error levels.