In this paper, we propose two rotation and scale invariant features extracted from the Hough transform domain to guide a CBIR system in the search of relevant building images. Upon receiving a query image, the CBIR system transforms the edges detected from the query into the Hough domain with 180 degrees/bins. From each bin, the peak percentage and peak distance ratio are calculated. The circular correlations between the peak percentages and peak distance ratios across the 180 bins of the query image and those of the database images are then taken as the similarity measure for ranking the relevance of the database images to the query.Index Terms-content based image retrieval (CBIR), feature extraction, matching algorithm, Hough transform, image database.
A moving fitting method for edge detection is proposed in this work. Polynomial function is used for the curve fitting of the column of pixels near the edge. Proposed method is compared with polynomial fitting method without sub-segment. The comparison shows that even with low order polynomial, the effects of moving fitting are significantly better than that with high order polynomial fitting without sub-segment.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.