A New Framework for Texture Based Image Content with Comparative Analysis of Clustering Techniques

The rapid development in computer technology for multimedia databases, digital media results in increase in the usage of digital images. Vast amount of data can be hidden in the form of digitized image, image mining is used to extract such kind of data and potential information from general collections of images. Image Clustering groups the images into classes of similar images without prior knowledge. Thus the search for the relevant information in the large space of image databases become more challenging and interesting too. This paper discusses the comparison between two partition clustering algorithm (K-Means and SOM) and one Hierarchical clustering algorithm using the texture as image features. The visual content of an image is analyzed in terms of low-level features extracted from the image. For texture feature extraction novel algorithm by pyramid-structured wavelet is presented. The SOM clustering algorithm produces better results, which is very much acceptable in image domain.