22 May 2014 Effectiveness of image features and similarity measures in cluster-based approaches for content-based image retrieval
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Abstract
Content-based image retrieval is an automatic process of retrieving images according to image visual contents instead of textual annotations. It has many areas of application from automatic image annotation and archive, image classification and categorization to homeland security and law enforcement. The key issues affecting the performance of such retrieval systems include sensible image features that can effectively capture the right amount of visual contents and suitable similarity measures to find similar and relevant images ranked in a meaningful order. Many different approaches, methods and techniques have been developed as a result of very intensive research in the past two decades. Among many existing approaches, is a cluster-based approach where clustering methods are used to group local feature descriptors into homogeneous regions, and search is conducted by comparing the regions of the query image against those of the stored images. This paper serves as a review of works in this area. The paper will first summarize the existing work reported in the literature and then present the authors’ own investigations in this field. The paper intends to highlight not only achievements made by recent research but also challenges and difficulties still remaining in this area.
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Hongbo Du, Hanan Al-Jubouri, Harin Sellahewa, "Effectiveness of image features and similarity measures in cluster-based approaches for content-based image retrieval", Proc. SPIE 9120, Mobile Multimedia/Image Processing, Security, and Applications 2014, 912008 (22 May 2014); doi: 10.1117/12.2057721; https://doi.org/10.1117/12.2057721
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