Content-Based Image Retrieval Using Invariant Color and Texture Features

Since the last decade, Content-Based Image Retrieval was a hot topic research. The computational complexity and the retrieval accuracy are the main problems that CBIR systems have to avoid. To avoid these problems, this paper proposes a new content-based image retrieval method that uses both color and texture feature. To extract the color feature from the image, the color moment will be calculated where the image will be in the HSV color space. To extract the texture feature, the image will be in gray-scale and Ranklet Transform is performed on it. From the ranklet images generated from the original image, the texture feature is extracted by calculating the texture moments. Experiments results show that using both color and texture feature to describe the image and use them for image retrieval is more accurate than using one of them only.