doi: 10.7763/IJCTE.2010.V2.227
Image Retrieval using Texture Features extracted from GLCM, LBG and KPE
H. B. Kekre1
,
Sudeep D. Thepade1 ,
Tanuja K. Sarode1 ,
Vashali Suryawanshi2
- 1School of Technology Management and Engineering, SVKM's NMIMS University, Mumbai-56, INDIA.
- 2College, Bandra(w), Mumbai, INDIA.
Abstract
In this paper a novel method for image retrieval based on texture feature extraction using Vector Quantization (VQ) is proposed. We have used Linde-Buzo-Gray (LBG) and Kekre's Proportionate Error (KPE) algorithms for texture feature extraction. The image is first divided into pixel blocks of size 2X2, each pixel with red, green and blue component. Atraining vector of dimensions 12 is created using this block. Collection of such training vectors is a training set. To generate the texture feature vector (size of codebook 16X12) of the image, popular LBG and KPE algorithms are applied on the initial training set. Results are compared with the Gray Level Co-occurance Matrix (GLCM) method. The proposed method requires 89.10% less computations compared to the GLCM method. The LBG and KPE based image retrieval techniques give higher precision and recall values than GLCM based method, which concludes that the proposed techniques give better texture feature discrimination capability than GLCM.
Keywords
- CBIR
- Vector Quantization
- GLCM
- LBG
- KPE
How to Cite
H. B. Kekre, Sudeep D. Thepade, Tanuja K. Sarode, and Vashali Suryawanshi, "Image Retrieval using Texture Features extracted from GLCM, LBG and KPE," International Journal of Computer Theory and Engineering, vol. 2, no. 5, pp. 695-700, 2010. https://doi.org/10.7763/IJCTE.2010.V2.227
Copyright & License
Copyright © 2010 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).