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