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. A training 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.
The Vector Quantization (VQ) is applied in the work of REF for feature extraction.
Image Retrieval using Texture Features extracted from GLCM, LBG and KPE
International Journal of Computer Theory and Engineering
Computer Science