Augmented Feature Fusion for Image Retrieval System

The performance of current image retrieval system is largely determined by the quality and discriminative capability of features. Therefore, using what features and how to effectively combine the power of appropriate features are important in the system. We adopt the reciprocal neighbor based graph fusion approach for feature fusion. More importantly, we explicitly augment the original approach with the following two strategies: 1) we investigate the most suitable feature combinations on various datasets, including the deep learning feature, which has been popular for image retrieval recently; 2) we further improve the robustness of original graph fusion approach by the SVM prediction strategy. Extensive experiments are performed on three benchmark datasets including UKbench, Holidays and Corel-5K, to validate the impressive performance of the augmented feature fusion. On the three datasets, our retrieval system significantly outperforms several existing algorithms. For example on UKbench, the N-S score of our approach achieves 3.88, which is one of the highest accuracies to the best of our knowledge.