An Efficient CBIR Technique with YUV Color Space and Texture Features

Abstract

In areas of government, academia and hospitals, large collections of digital images are being created. These image collections are the product of digitizing existing collections of analogue photographs, diagrams, drawings, paintings, and prints. Retrieving the specified similar image from a large dataset is very difficult. A new image retrieval system is presented in this paper, which used YUV color space and wavelet transform approach for feature extraction. Firstly, the color space is quantified in non-equal intervals, then constructed one dimension feature vector and represented the color feature. Similarly, the texture feature extraction is obtained by using wavelet. Finally, color feature and texture feature are combined based on wavelet transform. The image retrieval experiments specified that visual features were sensitive for different type images. The color features opted to the rich color image with simple variety. Texture feature opted to the complex images. At the same time, experiments reveal that YUV texture feature based on wavelet transform has better effective performance and stability than the RGB and HSV. The same work is performed for the RGB and HSV color space and their results are compared with the proposed system. The result shows that CBIR with the YUV color space retrieves image with more accuracy and reduced retrieval time.

Keywords---Content based image retrieval, Wavelet transforms, YUV, HSV, RGB