Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Relevance Feedback for Spine X-ray Retrieval
CBMS '05 Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems
A survey of content-based image retrieval with high-level semantics
Pattern Recognition
IEEE Transactions on Image Processing
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
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One of the main challenges for Content-Based Image Retrieval (CBIR) is to achieve meaningful mappings between the high-level semantic concepts and the low-level visual features in images. This paper presents an approach for bridging this semantic gap to improve retrieval quality using the Ranking Support Vector Machine (Ranking SVM) algorithm. Ranking SVM is a supervised learning algorithm which models the relationship between semantic concepts and image features, and performs retrieval at the semantic level. We apply it to the problem of vertebra shape retrieval on a digitized spine x-ray image collection from the second National Health and Nutrition Examination Survey (NHANES II). The experimental results show that the retrieval precision is improved 2.45-15.16% using the proposed approach.