Content Based Image Retrieval using Combined Features of Color and Texture Features with SVM Classification R. Usha [1] K. Perumal [2] Research Scholar [1] Associate Professor [2] Madurai Kamaraj University, Madurai. [email protected], [email protected]. Abstract 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. 1. Introduction Nowadays in digital photography, to save bulk of large amounts of high quality images, network speed and storage capacity has been made possible. Digital images are used in a wide range of applications such as geography, medical, architecture, advertising, design, military and albums. However here we have some difficulties in searching and organizing the largest quantity of images in databases. Generally the retrieval of image is classified into two methods such as 1. Text Based Image Retrieval and 2. Content Based Image Retrieval. Text Based Image Retrieval is having following disadvantages such as inefficiency, loss of information, time consuming process and more expensive task. These problems are overcome by using Content Based Image Retrieval for image retrieval. “Content based” refers that the search will analyse the contents of an image rather than the data about image such as keywords, tags, name of file extension like jpg, bmp, gif etc. Here the „content‟ refers visual informations such as color, texture and shape that can be derived from the image itself. Therefore, in this paper we proposed effective CBIR system using color and texture feature to overcome these above mentioned drawbacks of Text based image Retrieval. 2. Related works Image retrieval in CBIR based on the visual features such as texture, color and shape. In this work we choose two visual features as texture and color. Texture analysis, is generally a very time-consuming process. Research in texture analysis is very important, because that is used to improve the discriminatory ability of the extracted image features. There are three primary issues in texture analysis, such as texture classification, texture segmentation and shape recovery from texture. Texture classification, is a process of identifying the given texture region from a given set of texture classes. Texture segmentation is concerned with automatically determining the boundaries between various textured regions in an image [1]. In order to accurately capture the textural characteristics of an image, texture analysis algorithms use filter banks or co-occurrence gray level matrices (GLCMs) have to consider multiple orientations and scales. The computational cost overhead for applying this method may be heavy. It is also reported in [2] that SFTA works much faster in terms of feature extraction time, when compared to Gabor and Haralick methods. The main objective of the SFTA is to extract texture feature in an image which results in the formation of a feature vector. Haussdorf fractal dimension method is used in SFTA. To find optimal threshold Otsu algorithm is used. It is suggested in [3] R Usha et al , International Journal of Computer Science & Communication Networks,Vol 4(5),169-174 169 ISSN:2249-5789
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Content Based Image Retrieval using Combined Features of Color and Texture