A classification-driven similarity matching framework for retrieval of biomedical images

This paper presents a classification-driven biomedical image retrieval system to bride the semantic gap by transforming image features to their global categories at different granularity, such as image modality, body part, and orientation. To generate the feature vectors at different levels of abstraction, both the visual concept feature based on the "bag of concepts" model that comprise of local color and texture patches and various low-level global color, edge, and texturerelated features are extracted. Since, it is difficult to find a unique feature to compare images effectively for all types of queries, we utilize a similarity fusion approach based on the linear combination of individual features. However, instead of using the commonly used fixed or hard weighting approach, we rely on the image classification to determine the importance of a feature at real time. For this, a supervised multi-class classifier based on the support vector machine (SVM) is trained on a set o...

• Type: Conference
• Year: 2010
• Where: MIR
• Authors: Md. Mahmudur Rahman, Sameer Antani, George R. Thoma
• Keywords: Biomedical Image Retrieval, Image, MIR 2010, Multimedia, Similarity Fusion Approach