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

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          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.

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          Multi technique amalgamation for enhanced information identification with content based image data

          Image data has emerged as a resourceful foundation for information with proliferation of image capturing devices and social media. Diverse applications of images in areas including biomedicine, military, commerce, education have resulted in huge image repositories. Semantically analogous images can be fruitfully recognized by means of content based image identification. However, the success of the technique has been largely dependent on extraction of robust feature vectors from the image content. The paper has introduced three different techniques of content based feature extraction based on image binarization, image transform and morphological operator respectively. The techniques were tested with four public datasets namely, Wang Dataset, Oliva Torralba (OT Scene) Dataset, Corel Dataset and Caltech Dataset. The multi technique feature extraction process was further integrated for decision fusion of image identification to boost up the recognition rate. Classification result with the proposed technique has shown an average increase of 14.5 % in Precision compared to the existing techniques and the retrieval result with the introduced technique has shown an average increase of 6.54 % in Precision over state-of-the art techniques.
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            Author and article information

            Contributors
            Role: Editor
            Journal
            PLoS One
            PLoS ONE
            plos
            plosone
            PLoS ONE
            Public Library of Science (San Francisco, CA USA )
            1932-6203
            2016
            17 June 2016
            : 11
            : 6
            : e0157428
            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
            Stanford University Medical Center, UNITED STATES
            Author notes

            Competing Interests: The authors have declared that no competing interests exist.

            Conceived and designed the experiments: NA RS. Performed the experiments: NA RS. Analyzed the data: NA RS KBB SAC MR HAH ZI. Contributed reagents/materials/analysis tools: NA RS KBB SAC MR HAH ZI. Wrote the paper: NA RS.

            Article
            PONE-D-15-52972
            10.1371/journal.pone.0157428
            4912113
            27315101
            13da791c-df2a-4898-aea3-3a6c68792ea6
            © 2016 Ali et al

            This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

            History
            : 6 December 2015
            : 31 May 2016
            Page count
            Figures: 16, Tables: 8, Pages: 20
            Funding
            Funded by: funder-id http://dx.doi.org/10.13039/501100004681, Higher Education Commission, Pakistan;
            Award ID: PIN No. IRSIP 28 ENGG 03
            Award Recipient :
            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.
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