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One of the challenges in Content-Based Image Retrieval (CBIR) is to reducethe semantic gaps between low-level features and high-level semantic concepts.In CBIR, the images are represented in the feature space and the performance ofCBIR depends on the type of selected feature representation. Late fusion alsoknown as visual words integration is applied to enhance the performance ofimage retrieval. The recent advances in image retrieval diverted the focus ofresearch towards the use of binary descriptors as they are reportedcomputationally efficient. In this paper, we aim to investigate the late fusionof Fast Retina Keypoint (FREAK) and Scale Invariant Feature Transform (SIFT).The late fusion of binary and local descriptor is selected because among binarydescriptors, FREAK has shown good results in classification-based problemswhile SIFT is robust to translation, scaling, rotation and small distortions.The late fusion of FREAK and SIFT integrates the performance of both featuredescriptors for an effective image retrieval. Experimental results andcomparisons show that the proposed late fusion enhances the performances ofimage retrieval.

Content-Based Image Retrieval Based on Late Fusion of Binary and Local Descriptors