Material and textural features-based retrieval using feature similarity mapping for synthetic images

Abstract

Content Based image retrieval (CBIR) is one of the prominent application of image processing where image retrieval from the database is made automatic based on the content in it. The materialist details of the images contains high degree of information and can serve as important toll for analysis. Different variant of content based image retrieval work by locating an object in the image and then retrieving images with similar object in it. The method works using different similarity assessment techniques. In this research work, we have developed a new material feature and HMMD space CBIR technique which works by transforming the domain to HMMD space and then finding the material and Texture Features along with other relevant features. These features forms he feature vector and are then deployed to assess the similarity of the two images which then generates a relevant and irrelevant image search. The system has higher accuracy of assessment as verified by experimentation for precision, productivity and precision agriculture.