Multiple features-based image retrieval

Color and texture information are two important visual features of an image. In this paper, an efficient content-based image retrieval system is proposed based on color and texture feature. The color feature is extracted by quantifying the HSV color space into non-equal intervals and the color feature is represented by color histogram. Texture feature is obtained by local binary pattern (LBP). When computing the similarity between query image and target images in the database, Gaussian normalization is exploited on the feature space and distant space. And then the linear combination of normalized distances for color and texture is performed to obtain the similarity as the index of image. The exhaustive search scheme is used for retrieval, and the evaluation criterion is precision and recall about the number of returned images. The results of experiments demonstrate the efficiency of the proposed system.