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Content-based Image Retrieval using SURF Feature Point Descriptor and Local Binary Pattern with Color Histogram
Abstract
An important research issue in multimedia technology is the retrieval of similar objects. Most of the content-based image retrieval (CBIR) uses the low-level features such as color, shape and texture to extract the features from the images. A short time ago, the interest points are used to extract the most relevant images with different view point and transformations. Speed up Robust Feature (SURF) is robust and fast interest points detector/descriptor used in many computer vision applications. In this paper, SURF feature points descriptors are computed and then convex hull is used to make region of interest. The low level information inside region of interest is used with color histogram. A texture feature local binary pattern (LBP) is also combined with color histogram to make better retrieval result.
Cite this Article
Verma V, Johari PK. Content-Based Image Retrieval using SURF Feature Point Descriptor and Local Binary Pattern with Color Histogram. Journal of Open Source Developments. 2015; 2(2): 9–15p.
Keywords
References
Gerard S, Buckely C. Term-Weighting Approaches in Automatic Text Retrieval. Information Processing and Management. 1988; 24(5): 513–23p.
Long F, Zhang H, Dagan H, et al. Fundamentals of Content Based Image Retrieval. In: Feng DD, Siu WC, Zhang HJ (Eds.). Multimedia Information Retrieval and Management. Berlin Heidelberg, New York: Springer-Verlag; 2003.
Jyothi B, Latha MY, Reddt VSK. Relevance Feedback Content Based Image Retrieval Using Multiple Feaatures. Proceedings of the 2010 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC); 2010 Dec 28–29; Coimbatore, India.
Bay H, Ess A, Tuytelaars T, et al. Speeded Up Robust Features (SURF). Computer Vision and Image Understanding (CVIU). 2008; 110(3): 346–59p.
Song J, Lu X, Ling H, et al. Envelope Extraction for Composite Shapes for Shape Retrieval. Proceedings of the 21st International Conference on Pattern Recognition (ICPR 2012); 2012 Nov 11–15; Tsukuba Science City, Japan.
Song YJ. Content-based image retrieval using new color histogram. Proceedings of the 2004 International Symposium on Intelligent Signal Processing and Communications Systems; 2004 Nov 18–19; Seoul, South Korea. 609–11p.
Asha S, Sreeraj M. Content Based Video Retrieval using SURF Descriptor. Proceedings of the 3rd International Conference on Advances in Computing and Communications (ICACC); 2013 Aug 29–31; Cochin, India. 212–15p.
Velmurugan K, Baboo SS. Content-Based Image Retrieval using SURF and Colour Moments. Global Journal of Computer Science and Technology. 2011; 11(10): 1–4p. 9. Takala V, Ahonen T, Pietikainen M. Block-Based Methods for Image Retrieval Using Local Binary Patterns. Berlin Heidelberg, New York: Springer; 2005. 882–91p.
Ojala T, Pietik¨ainen M, Hardwood D. A Comparative Study of Texture Measures with Classification Based on Feature Distribution. Pattern Recognition. 1996; 29: 51–9p.
http://wang.ist.psu.edu/~jwang/test1.tar.
MPEG Video Group. Description of Core Experiments for PEG-7 Color Texture Descriptors. ISOIMPEGJTC IISC29/Wgll MPEG98/M2819; 1999.
Anees MV, Kumar GS, Sreeraj M. Automatic Image Annotation Using SURF Descriptors. Proceedings of the 2012 Annual IEEE India Conference (INDICON); 2012 Dec 7–9; Kochi, India. 920–24p.
Alfanindya A, Hashim N, Eswaran C. Content Based Image Retrieval And Classification using Speeded-Up Robust Features (SURF) and Grouped Bag-of-Visual-Words (GBoVW). Proceedings of the International Conference on Technology, Informatics, Management, Engineering & Environment; 2013 Jun 23–26; Bandung, Indonesia. 77–82p.
Yuasa K, Wada T. Keypoint Reduction for Smart Image Retrieval. Proceedings of the IEEE International Symposium on Multimedia; 2013 Dec 9–11; Anaheim, CA. 351–8p.
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