A novel efficient technique for extracting valid feature information
In this study, we proposed a quick and accurate algorithm for content-based image classification. The proposed method is also used to retrieve similar images from databases. In this paper color and texture information are used to represent image features. The basic idea is to extract color information about global and local features of images. A global color feature is extracted by an RGB model. While, a local color feature is extracted by an HSV model. In the case of a local feature, if it cannot be classified, the result is inaccurate retrieval. A GA (genetic algorithm) is used to extract local features which can be classified. Local features extracted by a GA are optimal representative features. In the experiment, the accuracy of image classification is measured using the proposed algorithm. Also, we compared the previous algorithm with the proposed algorithm in terms of image classification performance. As a result, the proposed algorithm showed higher performance in terms of accuracy.
feature information genetic algorithm image retrieval support vector machine