CONTENT BASED IMAGE RETRIEVAL (CBIR) USING MULTIPLE FEATURES FOR TEXTILE IMAGES BY USING SVM CLASSIFIER

ABSTRACT

In this project, we proposed a Content Based Image Retrieval (CBIR) system which is used to retrieve a relevant image from an outsized database. Textile images showed the way for the development of CBIR. It establishes the efficient combination of color, shape and texture features. Here the textile image is given as dataset. The images in database are loaded. The resultant image is given as input to feature extraction technique which is transformation of input image into a set of features such as color, texture and shape. The texture feature of an image is taken out by using Gray level co-occurrence matrix (GLCM). The color feature of an image is obtained by HSI color space. The shape feature of an image is extorted by sobel technique. These algorithms are used to calculate the similarity between extracted features. These features are combined effectively so that the retrieval accuracy and recall rate is enhanced. The classification techniques such as Support Vector Machine (SVM) are used to classify the features of a query image by splitting the group such as color, shape and texture. Finally, the relevant images are retrieved from a large database and hence the efficiency of an image is plotted.The software used is MATLAB 7.10 (matrix laboratory) which is built software applications

KEYWORDS

CBIR, HSI color space, SVM, Feature extraction.