An Efficient Approach of Content Based Image Retrieval using Texture, Color and Shape Features of an Image

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2017 by IJETT Journal
Volume-48 Number-6
Year of Publication : 2017
Authors : Suresh M B, Dr.B Mohankumar Naik
DOI :  10.14445/22315381/IJETT-V48P256


Suresh M B, Dr.B Mohankumar Naik "An Efficient Approach of Content Based Image Retrieval using Texture, Color and Shape Features of an Image", International Journal of Engineering Trends and Technology (IJETT), V48(6),316-320 June 2017. ISSN:2231-5381. published by seventh sense research group

retrieval of an image is an extra powerful and green for dealing with enormous photograph database. content based photograph retrieval (CBIR) is a one of the picture retrieval technique which makes use of consumer visible capabilities of an photograph together with color, form, and texture functions and so forth. it permits the quit user to offer a query photograph so one can retrieve the saved photos in database in keeping with their similarity to the question photograph. on this work, content based totally image retrieval is done by means of combining the two functions including color and texture. Color capabilities are extracted by way of using HSV histogram, color correlogram and color second values. Texture capabilities are extracted through segmentation based fractal texture analysis (SFTA). The accuracy of color histogram based totally matching can be accelerated by way of the use of shade coherence vector (CCV) for successive refinement. The velocity of shape based retrieval may be enhanced by way of considering approximate form in place of the exact form. The principle goal this work is classification of photo the use of SVM algorithm.


[1] MihranTuceryan and Anil K.Jain, Texture Analysis, The Handbook of Pattern Recognition and Computer Vision, pp.207-248, 1998.
[2] AlceuFerraz Costa, Gabriel Humpire-Mamani, Agma Juci Machado Traina, “An Efficient Algorithm for Fractal Analysis of texture “, Graphics, Patterns and Images (SIBGRAPI), 2012 25th SIBGRAPI Conference, pp. 39 -46, 2012.
[3] C. Traina Jr., A. J. M. Traina, L. Wu, and C. Faloutsos, “Fast feature selection using fractal dimension,” in Brazilian Symposium on Databases (SBBD), João Pessoa, Brazil, 2000, pp. 158–171.
[4] P. K. Sahoo, S. Soltani, A. K. C. Wong, and Y. Chen, “A survey of thresholding techniques,” Computer Vision Graphics Image Processing, Vol. 41, 1988, pp. 233-260.
[5] N. Otsu. A threshold selection method from gray- level histogram. IEEE Trans. Systems Man Cybern.
[6] Y. A. Aslandogan and C. T. Yu, "Techniques and Systems for Image and Video Retrieval," IEEE Transactions on Knowledge and Data Engineering, Vol. 11, Issue 1, pp. 56-63, Jan/Feb 1999.
[7] A. J. M. Traina, A. G. R. Balan, L. M. Bortolotti, and C. Traina Jr., “Content- based Image Retrieval Using Approximate Shape ofObjects”, Proceedings of the 17th IEEE Symposium on Computer- Based Medical Systems, pp. 91-96, 2004.
[8] Dengsheng Zhang and Guojun Lu, “Review of shape representation andd escription techniques”, Pattern Recognition Society. Published by Elsevier Ltd, Vol. 37, pp. 1-19, 2004.
[9] Sarfraz and M. Ridha “Content-Based Image Retrieval Using Multiple Shape Descriptors”, IEEE/ACS International Conference On Computer Systems and Applications, pp. 730-737, 2007.
[10] Xiaoqian Xu, Dah-Jye Lee, Sameer Antani, and L. Rodney Long, “A Spine X-Ray Image Retrieval System Using Partial ShapeMatching”, IEEE Transactions On Information Technology In Biomedicine, Vol. 12, Issue 1, pp. 100-108, January 2008.

Image Retrieval; Content based image retrieval; HSV color histogram; color correlogram; color moments; SVM Algorithm; Relative Standard Derivation; Fractal Texture features.