A Proposal of an Efficient Feature Extracting Method for Content-Based Image Retrieval


Searching a required image from the World Wide Web (WWW) is very difficult because the WWW contains a huge number of images. To solve such a problem, an efficient system is needed to retrieve images that are required by the user. The content-based image retrieval (CBIR) system has been used to solve this problem. In this paper, a new combination of three techniques is used for visual features extracting. Color histogram was used to extract color feature from the image. Multi wavelet transform was chosen to represent the information of the texture and the edge histogram was used to represent the shape feature. Object scaling and translation in an image can be got robustly by the combination of these techniques. Furthermore, to speed up retrieval and similarity computation of the proposed system, the data set images are clustered using k-mean clustering algorithm according to the weighted feature vectors. The system evaluation experimentally carried out on800Wang color image dataset, and showed that proposed system performed significantly better and faster than other existing systems by using the proposed features.