Content Based Image Retrieval using Combined Features of Color and Texture Features with SVM Classification

Retrieval of an image is a more effective and efficient for managing extensive image database. Content Based Image Retrieval (CBIR) is a one of the image retrieval technique which uses user visual features of an image such as color, shape, and texture features etc. It permits the end user to give a query image in order to retrieve the stored images in database according to their similarity to the query image. In this work, content based image retrieval is accomplished by combining the two features such as color and texture. Color features are extracted by using hsv histogram, color correlogram and color moment values. Texture features are extracted by Segmentation based Fractal Texture Analysis (SFTA). The combined features which are made up of 32 histogram values,64 color correlogram values, 6 color moment values and 48 texture features are extracted to both query and database images. The extracted feature vector of the query image is compared with extracted feature vectors of the database images to obtain the similar images. The main objective this work is classification of image using SVM algorithm. Keywords— Image Retrieval; Content based image retrieval; HSV color histogram; color correlogram; color moments; SVM Algorithm; Relative Standard Derivation; Fractal Texture features.