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

dc.contributor.authorAli, Nouman
dc.contributor.authorBajwa, Khalid Bashir
dc.contributor.authorSablatnig, Robert
dc.contributor.authorChatzichristofis, Savvas A.
dc.contributor.authorIqbal, Zeshan
dc.contributor.authorRashid, Muhammad
dc.contributor.authorHabib, Hafiz Adnan
dc.description.abstractWith 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.en_UK
dc.relation.ispartofseriesMathematical Problems in Engineering;
dc.rightsCopyright © 2016 Zahid Mehmood et al.en_UK
dc.subjectVisual Words Integrationen_UK
dc.subjectScale Invariant Feature Transform (SIFT)en_UK
dc.subjectSpeeded-Up Robust Features (SURF)en_UK
dc.subjectContent-Based Image Retrieval (CBIR)en_UK
dc.titleA Novel Image Retrieval Based on Visual Words Integration of SIFT and SURFen_UK

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Copyright © 2016 Zahid Mehmood et al.
Except where otherwise noted, this item's license is described as Copyright © 2016 Zahid Mehmood et al.