Combining Color Quantization with Curvelet Transform for Image Retrieval

Color and shape descriptions of an image are the most widely used visual features in content-based image retrieval systems. Feature vectors for shape and color can be combined to improve the performance of the content-based image retrieval systems. In this paper, a novel image retrieval method integrating HSV color quantization and curve let transform is proposed. By analyzing properties of HSV(Hue, Saturation, Value) color space, a new dividing method to quantize the HSV color space into 24 non-uniform bins based on HSV soft decision is introduced and used for color histogram generation. Digital curve let transform is employed for extracting shape features in images, as it has been proved that the curve let transform is an almost optimal sparse representation of objects with edges. The generated HSV color histogram and the curve let feature are then combined and weighted for image retrieval, using Manhattan distance metric as the similiarity measure. Experiments on an image database of 565 images show that the combined feature performs well in precision and adaptability.