Image Retrieval using Fractional Energy of Column Mean of Row transformed Image with Six Orthogonal Image Transforms

The thirst of better and faster retrieval techniques has always fuelled to the research in content based image retrieval (CBIR). The paper presents innovative content based image retrieval (CBIR) techniques based on feature vectors as fractional coefficients of column mean of row transformed images using Discrete Cosine, Walsh, Haar, Slant, Discrete Sine, and Hartley transforms. Here the advantage of energy compaction of low frequency coefficients in transform domain is taken to greatly reduce the feature vector size per image by taking fractional coefficients of column mean of row transformed image. The feature vectors are extracted in six different ways from the column mean of row transformed image, with the first being considering all the coefficients of column mean of row transformed image and then six reduced coefficients sets (as 50%, 25%, 12.5%, 6.25%, 3.125%, 1.5625% of complete column mean of row transformed image) are considered as feature vectors. The six transforms are applied on the colour components of images to extract column mean of row transformed RGB plane respectively. Instead of using all coefficients of transformed images as feature vector for image retrieval, these six reduced coefficients sets for 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 10 categories. For each proposed CBIR technique 40 queries (4 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 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 all other discussed transforms in performance with highest precision and recall values for 25% of fractional coefficients.