Content Based Image Retrieval Using Color Moments and Texture

This paper proposes an algorithm to retrieve the color images from the database using color moments and the texture of color image. The color moments integrates the mean, standard deviation and the skewness to estimate the brightness and the intensity of the image. The texture descriptors are calculated using the energy and entropy values from the curvelet coefficients. Finally, similarity measure is computed and compared with all the images present in the database to classify the feature vectors. Experimental results show that the proposed algorithm is more accurate and efficient in retrieving the images with the user interested objects. Evaluation metrics used are Precision and Time taken to retrieval. Keywords―Color moments, Texture, Curvelet Transform, Similarity measure, Energy and Entropy values and Precision. І. INTRODUCTION Digital images require a large amount of data to be stored and the users are able to find images quickly and accurately from the large number of images . In early years, image retrieval was done by the textual annotators using the keywords. Here, increasing the number of digital images stored is not feasible. So there are some limitations in using the textual annotators. To overcome this problem, the image retrieval process (1) can be taken by the Content Based Image Retrieval System (CBIR)(2). The objective of the content based image retrieval system is to extract the features and can classify the images for retrieving the similar images related to the input query image. CBIR system is widely used in many modern technologies where the large amount of images is to be used. This system utilizes the features like color, texture(3), shape and spatial relationships to extract the relevant images from the database(4). Color feature is not only closely related to objects in an image, but also dependent on size of an image, orientations and the angle (5). Color feature has the robustness and the common representations of color features are color histograms, color moments and dominant color etc.,(6) these features are compared to similarity measure using the Euclidean distance measurement (7). This categorization of images can be processed by many techniques such as SVM (Support Vector Machine) kernel(8), Decision tree. There are some limitations in feature extraction (9), so the curvelet transform technique integrates the texture classification (10) effectively by evaluating the energy and entropy values as described later (11). Discrete curvelet transform (12) produces accurate results than the existing one. ІІ. DIFFERENT CBIR METHODS In a typical retrieval system, the query image and the images stored in the database are pre-processed automatically to extract the features using the curvelet. The query image is also processed same as the database images. The feature vectors are used to classify the images. And relevant images are retrieved based on the similarity measures from the database. In the image retrieval system, there are many approaches used to retrieve the feature based images. There are different CBIR methods used for determining the features like color and texture. Color feature is evaluated using the primitives of the color moments using the known RGB color model. Many methods have been proposed to extract the texture features either directly or from the image statistics, for example co-occurrence matrix or spatial frequency domain. The different methods used for feature extraction are: Markov Random Fields parameters, Gabor multichannel features, fractal-based features and co-occurrence features. ІІІ. PROPOSED METHOD The proposed algorithm is modification of existing method Sasikumar et al method [1], which uses Discrete Sine Transform (DST). The developed algorithm is more efficient in comparison with [1]. Fig.1 shows the block diagram of an image retrieval system using the Curvelet 2812 Vol. 3 Issue 2, February 2014 International Journal of Engineering Research & Technology (IJERT) IJ E R T IJ E R T ISSN: 2278-0181 www.ijert. org IJERTV3IS21051 Transform. The color moments and Texture of an image are estimated as follows.