Preprint Article Version 2 This version not peer reviewed

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

Version 1 : Received: 19 March 2017 / Approved: 20 March 2017 / Online: 20 March 2017 (09:44:17 CET)
Version 2 : Received: 20 March 2017 / Approved: 21 March 2017 / Online: 21 March 2017 (03:49:41 CET)

How to cite: Ali, N.; Ali Mazhar, D.; Iqbal, Z.; Ashraf, R.; Ahmed, J.; Zeeshan Khan, F. Content-Based Image Retrieval Based on Late Fusion of Binary and Local Descriptors. Preprints 2017, 2017030156 (doi: 10.20944/preprints201703.0156.v2). Ali, N.; Ali Mazhar, D.; Iqbal, Z.; Ashraf, R.; Ahmed, J.; Zeeshan Khan, F. Content-Based Image Retrieval Based on Late Fusion of Binary and Local Descriptors. Preprints 2017, 2017030156 (doi: 10.20944/preprints201703.0156.v2).

Abstract

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.

Subject Areas

CBIR;, Late Fusion; SVM; BOVW

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