A Novel Image Retrieval Based on Visual Words Integration of SIFT and SURF

Nouman Ali; Khalid Bashir Bajwa; Robert Sablatnig; Savvas A Chatzichristofis; Zeshan Iqbal; Muhammad Rashid; Hafiz Adnan Habib

doi:10.1371/journal.pone.0157428

A Novel Image Retrieval Based on Visual Words Integration of SIFT and SURF

PLoS One. 2016 Jun 17;11(6):e0157428. doi: 10.1371/journal.pone.0157428. eCollection 2016.

Authors

Nouman Ali^{1

2}, Khalid Bashir Bajwa¹, Robert Sablatnig², Savvas A Chatzichristofis³, Zeshan Iqbal¹, Muhammad Rashid⁴, Hafiz Adnan Habib¹

Affiliations

¹ Faculty of Telecommunication and Information Engineering, University of Engineering and Technology, Taxila, Pakistan.
² Institute of Computer Aided Automation, Computer Vision Lab, Vienna University of Technology, Vienna, Austria.
³ Department of Electrical and Computer Engineering, Democritus University of Thrace, Xanthi, Greece.
⁴ Department of Computer Engineering, Umm Al Qura University, Makkah, Saudi Arabia.

Abstract

With the recent evolution of technology, the number of image archives has increased exponentially. In Content-Based Image Retrieval (CBIR), high-level visual information is represented in the form of low-level features. The semantic gap between the low-level features and the high-level image concepts is an open research problem. In this paper, we present a novel visual words integration of Scale Invariant Feature Transform (SIFT) and Speeded-Up Robust Features (SURF). The two local features representations are selected for image retrieval because SIFT is more robust to the change in scale and rotation, while SURF is robust to changes in illumination. The visual words integration of SIFT and SURF adds the robustness of both features to image retrieval. The qualitative and quantitative comparisons conducted on Corel-1000, Corel-1500, Corel-2000, Oliva and Torralba and Ground Truth image benchmarks demonstrate the effectiveness of the proposed visual words integration.

MeSH terms

Algorithms
Archives
Artificial Intelligence
Image Interpretation, Computer-Assisted*
Image Processing, Computer-Assisted*
Information Storage and Retrieval*
Pattern Recognition, Automated*
Support Vector Machine

Grants and funding

The authors would like to thank Higher Education Commission (HEC) Pakistan for a fellowship grant (PIN No. IRSIP 28 ENGG 03 sanctioned in favor of Nouman Ali) for performing the research work at Institute of Computer Aided Automation, Computer Vision Lab, Technical University Vienna, Austria.