Sectorization of Full Walsh Transform for Feature Vector Generation in CBIR

We have introduced a novel idea of sectorization of complex Full Walsh transformed components. In this paper we have proposed two different approaches along with augmentation of mean of zero and highest row components of row transformed values and mean of zero- and highest column components of Column transformed values for feature vector generation namely SS-CC Plane and SC-CS Plane. We have introduced the new performance evaluation parameters i.e. LIRS and LSRR apart from precision and Recall, the traditional methods. Two similarity measures such as sum of absolute difference and Euclidean distance are used and results are compared. The cross over point performance of overall average of precision and recall for both approaches on different sector sizes are compared. The Full walsh transform sectorization is experimented on both SS-CC and SC-CS Plane with augmentation and without augmentation for the color images. The algorithm proposed here is worked over database of 1055 images spread over 12 different classes. Overall Average precision and recall is calculated for the performance evaluation and comparison of 4, 8, 12 & 16 Full Walsh sectors. The use of Absolute difference as similarity measure always gives lesser computational complexity and density distribution approach with sum of absolute difference as similarity measure of feature vector has the best retrieval performance.