Feature Extraction And Similarity Measures for Content Based Image Retrieval

Abstract

Due to the enormous increase in image database sizes, as well as its vast deployment in various applications, the need for CBIR development arose. This thesis basically analyses the Content-based image retrieval (CBIR) techniques based on texture, shape and color. And implements CBIR based on texture and color as a case study. In designing the CBIR, feature extraction and Similarity Measures require a great deal for retrieval of Images. The solution initially proposed was to extract the primitive features of a query image and compare them to those of database images. The image features under consideration were colour, texture and shape. Thus, using matching and comparison algorithms, the colour, texture and shape features of one image are compared and matched to the corresponding features of another image. This comparison is performed using colour, texture and shape distance matrices. In the end, these matrices are performed one after another, so as to retrieve database images that match to the query. The similarity between features is to be calculated using algorithms used by well known CBIR systems. For each specific feature there is a specific algorithm for extraction and another for matching. In this thesis, we have used histogram representation for the color extraction, and wavelet feature representation for the texture representation.