Bridging the semantic gap using Ranking SVM for image retrieval

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.