An Automatic Color Feature Vector Classification Based on Clustering Method

In computer vision application, visual features such as shape, color and texture are extracted to characterize images. Each of the features is represented using one or more feature descriptors. One of the important requirements in image retrieval, indexing, classification, clustering, etc. is extracting efficient features from images. The color feature is one of the most widely used visual features. Use of color histogram is the most common way for representing color feature. One of disadvantage of the color histogram is that it does not take the color spatial distribution into consideration. In this paper an automatic color feature vector classification based on clustering approach is presented, which effectively describes the spatial information of color features. The image retrieval results are compare to improved color feature vector show the acceptable efficiency of this approach. It propose an automatic color feature vector classification of satellite images using clustering approach. The intention is to study cluster a set of satellite images in several categories on the color similarity basis. The images are processed using LAB color space in the feature extraction stage. The resulted color-based feature vectors are clustered using an automatic unsupervised classification algorithm. Some experiments based on the proposed recognition technique have also been performed. More research, however, is needed to identify and reduce uncertainties in the image processing chain to improve classification accuracy. The mathematical training and prediction analysis of a general familiarity with satellite classifications meet typical map accuracy standards.