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A Novel Image Retrieval Based on Visual Words Integration of SIFT and SURF
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
doi:10.1371/journal.pone.0157428
pmid:27315101
pmcid:PMC4912113
fatcat:mvmyraoarrh6bk6r6fmzaee47q