AN EFFICIENT IMAGE MATCHING TECHNIQUE IN MATLAB

Authors

  • Prof. Vikram Mahendra Kakade Assistant Professor, Electronics & Telecommunication Engineering Department, Prof Ram Meghe College of Engineering & Management, Badnera-Amravati, Maharashtra India-444702.

Keywords:

CBIR, Color, Feature, Image, Mapping

Abstract

The deployment of large image databases for a variety of applications have now become realizable. Databases of art works, satellite and medical imagery have been attracting more and more users in various professional fields — for example, geography, medicine, architecture, advertising, design, fashion, and publishing. In this paper my approach is used to present best method in terms of efficiency and comparative analysis over various method where work has been carried until now the retrieval of images based on visual features such as colour, texture and shape Reasons for its development . In many large image databases, traditional methods of image indexing have proven to be insufficient, laborious, and extremely time consuming. These old methods of image indexing, ranging from storing an image in the database and associating it with a keyword or number, to associating it with a categorized description, have become obsolete.

References

A. Diplaros, E. G. M. Petrakis, and E. Milios, ``Shape matchingwith occlusion in image databases,'' in Proc. Infotech Oulu Int.Workshop Inf. Retr. (IR), Sep. 2001, pp. 142_150.

N. Kumar et al., ``Leafsnap: A computer vision system for automatic plantspecies identi_cation,'' in Computer Vision (Lecture Notes in ComputerScience). Berlin, Germany: Springer-Verlag, 2012, pp. 502_516.

S. Belongie, J. Malik, and J. Puzicha, ``Shape matching and object recognitionusing shape contexts,'' IEEE Trans. Pattern Anal. Mach. Intell.,

vol. 24, no. 4, pp. 509_522, Apr. 2002.

C. C. Chang, S. M.Hwang, and D. J. Buehrer, ``Ashape recognition scheme based on relative distances of feature points from the centroid,'' Pattern Recognition., vol. 24, no. 11, pp. 1053_1063, 1991.

K.-L. Tan, B. C. Ooi, and L. F. Thiang, ``Retrieving similar shapes effectively and efficiently,'' Multimedia Tools Appl., vol. 19, no. 2, pp. 111_134,2003.

E. Attalla and P. Siy, ``Robust shape similarity retrieval based on contour segmentation polygonal multi resolution and elastic matching,'' Pattern Recognit., vol. 38, no. 12, pp. 2229_2241, Dec. 2005.

B. K. Jung, S. Y. Shin, W. Wang, H. D. Choi, and J. K. Pack, ``Similar MRI object retrieval based on moodified contour to centroid triangulation with arc difference rate,'' in Proc. 29th SAC, 2014, pp. 31_32.

J.-L. Shih and S.-Y. Lin, ``A new shape retrieval approach based on the multi-resolution contour-based descriptor,'' J. Inf. Technol. Appl., vol. 6,no. 2, pp. 40_51, 2012.

F. Mokhtarian and A. Mackworth, ``Scale-based description and recognitionof planar curves and two-dimensional shapes,'' IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-8, no. 1, pp. 34_43, Jan. 1986.

F. Mokhtarian and A. K. Mackworth, ``A theory of multiscale, curvaturebasedshape representation for planar curves,'' IEEE Trans. Pattern Anal.Mach. Intell., vol. 14, no. 8, pp. 789_805, Aug. 1992.

Additional Files

Published

15-06-2017

How to Cite

Prof. Vikram Mahendra Kakade. (2017). AN EFFICIENT IMAGE MATCHING TECHNIQUE IN MATLAB. International Education and Research Journal (IERJ), 3(6). Retrieved from http://ierj.in/journal/index.php/ierj/article/view/1087