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.
In this paper we propose partial yet efficient codebook search algorithm which uses sorting technique and uses only comparison. Our proposed algorithm does not use Euclidean distance computation and hence it is fastest as compared to other search methods ES, HOSM, DTPC. Form the results it is observed that proposed algorithm gives more MSE as compared to the exhaustive search method but with good execution speed. We also discuss codebook design methods LBG and FCG. The codebooks of different sizes 128, 256, 512 and 1024 are generated using LBG and FCG algorithm. Both the codebook generation algorithms are compared with respect to the execution speed. All the various search algorithms are implemented on the codebooks of different sizes 128, 256, 512 and 1024 obtained from LBG and FCG algorithms. From the results it is observed that FCG codebook gives better performance parameters MSE and PSNR as compared to LBG codebook and among the search algorithm proposed algorithm gives least time to encode the image with slight degradation in image quality.
The development and application of various remote sensing platforms result in the production of huge amounts of satellite image data. Therefore, there is an increasing need for effective querying and browsing in these image databases. In order to take advantage and make good use of satellite images data, we must be able to extract meaningful information from the imagery.Hence we proposed a new algorithm for SAR image segmentation. In this paper we propose segmentation using vector quantization technique on entropy image. Initially, we obtain entropy image and in second step we use Kekre's Fast Codebook Generation (KFCG) algorithm for segmentation of the entropy image. Thereafter, a codebook of size 128 was generated for the Entropy image. These code vectors were further clustered in 8 clusters using same KFCG algorithm and converted into 8 images. These 8 images were displayed as a result. This approach does not lead to over segmentation or under segmentation. We compared these results with well known Gray Level Co-occurrence Matrix. The proposed algorithm gives better segmentation with less complexity
Breast cancer is one of the major causes of death among women. An improvement of early diagnostic techniques is critical for women's quality of life. Mammography is the main test used for screening and early diagnosis. Contrast-enhanced magnetic resonance of the breast is the most attractive alternative to standard mammography. This paper presents a vector quantization segmentation method to detect cancerous mass from mammogram images. In order to increase radiologist's diagnostic performance, several computer-aided diagnosis (CAD) schemes have been developed to improve the detection of primary signatures of this disease: masses and microcalcifications.
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