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, Daniel L Rubin
2016 PLoS ONE  
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
more » ... esentations 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.
doi:10.1371/journal.pone.0157428 pmid:27315101 pmcid:PMC4912113 fatcat:mvmyraoarrh6bk6r6fmzaee47q