ABSTRACT

The paper presents innovative content based image retrieval (CBIR) techniques based on row mean of transformed column image as feature vector. For proposed CBIR techniques three different image transforms like Discrete Cosine Transform (DCT), Walsh Transform and Kekre Transform are considered here. For performance comparison the proposed CBIR techniques are tested on gray version of generic image database of 1000 images spread across 11 categories. For each image retrieval technique 55 queries (5 per category) were fired on the image database. Average precision and average recall values for all these queries are computed and used for performance comparison. The proposed CBIR method is considered with DC component as part of feature vector as well as without it. In all three transforms and variation of consideration/ignorance of DC coefficient results into total 6 novel proposed CBIR techniques. These techniques are compared with CBIR using full transformed image as feature vector. The results have shown the performance improvement (higher precision and recall values) with proposed methods compared to full transformed image as feature vector with great reduction in computational complexity. The negligence of DC component causes performance degradation in proposed techniques. The DCT with consideration of DC component gives best performance among the considered image transforms. The performance ranking of image transforms in proposed CBIR methods can be given as DCT, Walsh transform and Kekre transform.