Truncated DCT and Decomposed DWT SVD Features for Image Retrieval

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• 作者：N.S.T. Sai^a ; ^{hsainst@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; Ravindra Patil^b ; ^{ravi.patil212@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; Shailesh Sangle^b ; ^{sss.sangle@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; ^{bnemade@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; Bhushan Nemade^a ; ^b
• 关键词：CBIR ; DCT ; DWT ; SVD ; DCT-SVD ; DWT-SVD ; Overall Average Precision and Recall crossover point
• 刊名：Procedia Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：79
• 期：Complete
• 页码：579-588
• 全文大小：464 K

文摘

This paper describes the comparative study of Truncated DCT-SVD and DWT-SVD. In this paper we propose two different approaches to compute the feature vector for content based image retrieval (CBIR) system. SVD feature of successively truncated DCT image and DWT decomposed image computed for grayscale image, RGB and YCbCrcolor image. Truncated DCT and DWT decomposition SVD features of the image computed up to fifth level to compare the performance. Proposed methods incorporate with the multidimensional features vector computed by using SVD of low frequency coefficients of DCT and DWT of image. Similarity between the query image and database image measured here by using simple Euclidean distance and Bray Curtis Distance. The overall average precision and average recall crossover point of each image category. Proposed methods are tested on the augmented image database.