Content Based Image Retrieval Using Combined Features (Color and Texture)

Abstract — The purpose of this paper is to outline our research and solution to the problem of designing an Image Retrieval System based on combined features (color, texture) of image. Due to the massive increase in image database sizes, as well as its broad formation in various applications, the need for CBIR development arose. Firstly, this paper framework a description of the low level features of an image; texture, color. The color feature is extracted using histogram method, and makes a feature vector of color. Then Texture feature are extracted using GLCM (Gray Level Co-occurrence Matrix), and make a feature vector of texture. And then combined both (color, texture) feature vector. after that we use Euclidian distance formula for calculating distance between the combined feature vector(color, texture)of query image and combined feature vector(color, texture) of database image.After that, we sort these distance and most similar four images are displayed those have least distance. Our final result was a Mat Lab built software application that retrieves images from the database