Image Retrieval System based on Color Global and Local Features Combined with GLCM for Texture Features

In CBIR (content-based image retrieval) features are extracted based on color, texture, and shape. There are many factors affecting the accuracy (precision) of retrieval such as number of features, type of features (local or global), color model, and distance measure. In this paper, a two phases approach to retrieve similar images from data set based on color and texture is proposed. In the first phase, global color histogram is utilized with HSV (hue, saturation, and value) color model and an automatic cropping technique is proposed to accelerate the process of features extraction and enhances the accuracy of retrieval. Joint histogram and GLCM (gray-level co-occurrence matric) are deployed in phase two. In this phase, color features and texture features are combined to enhance the accuracy of retrieval. Finally, a new way of using K-means as clustering algorithm is proposed to classify and retrieve images. Two experiments are conducted using WANG database. WANG database consists of 10 different classes each with 100 images. Results of comparing the proposed approach with the most relevant approaches are promising.