Image Retrieval using Fractional Coefficients of Transformed Image using DCT and Walsh Transform

摘要

The paper presents innovative content based image retrieval (CBIR) techniques based on feature vectors as fractional coefficients of transformed images using DCT and Walsh transforms. Here the feature vector size per image is greatly reduced by taking fractional coefficients of transformed image. The feature vectors are extracted in fourteen different ways from the transformed image. Along with the first being all the coefficients of transformed image, seven reduced coefficients sets (as 50%, 25%, 12.5%, 6.25%, 3.125%, 1.5625% ,0.7813%, 0.39%, 0.195%, 0.097%, 0.048%, 0.024%, 0.012% and 0.06% of complete transformed image) are considered as feature vectors. The two transforms are applied on gray image equivalents and the colour components of images to extract Gray and RGB feature sets respectively. Instead of using all coefficients of transformed images as feature vector for image retrieval, these fourteen reduced coefficients sets for gray as well as RGB feature vectors are used, resulting into better performance and lower computations. The proposed CBIR techniques are implemented on a database having 1000 images spread across 11 categories. For each proposed CBIR technique 55 queries (5 per category) are fired on the database and net average precision and recall are computed for all feature sets per transform. The results have shown the performance improvement (higher precision and recall values) with fractional coefficients compared to complete transform of image at reduced computations resulting in faster retrieval. Finally Walsh transform surpasses DCT transforms in performance with highest precision and recall values for fractional coefficients and minimum number of computations up to 0.097% and then DCT takes over.