A Multi-Dimensional Similarity Modeling and Relevance Feedback Approach for Content-Based Image Retrieval.

This work presents a multi-dimensional similarity modeling strategy and relevance feedback technique for minimizing the semantic gap intrinsic problem of CBIR systems by allowing users to customize their queries according to their requirements and preferences. We propose a composite strategy using a multi-dimensional, vectorial, spatially clustered, and relevance-ordered approach. Given a set of k features which represents the images in an image database, the similarity measure between a query image and another from the image collection is analyzed in k components, and the images are ranked on a A dimensional space according to their projections over the axis x_n, where n = 1,2,... k. System experimentation was executed thoroughly using a test image database containing up to 12,000 pictures. The experimental results have shown that the presented approach can substantially improve the outcome in image retrieval systems.