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

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 concept...

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Detalles Bibliográficos
Autores principales: Ali, Nouman, Bajwa, Khalid Bashir, Sablatnig, Robert, Chatzichristofis, Savvas A., Iqbal, Zeshan, Rashid, Muhammad, Habib, Hafiz Adnan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4912113/
https://www.ncbi.nlm.nih.gov/pubmed/27315101
http://dx.doi.org/10.1371/journal.pone.0157428
Descripción
Sumario: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.