A new method for unsupervised image category clustering is presented, based on a continuous version of a recently introduced information theoretic principle, the information bottleneck (IB). The clustering method is based on hierarchical grouping: Utilizing a Gaussian mixture model, each image in a given archive is first represented as a set of coherent regions in a selected feature space. Imag...

Image segmentation is a basic but important preprocessing to image recognition in computer vision applications. In this paper, we propose a pixel-based k-means (PKM) clustering to generate superpixels, which comprise many pixels with similar colors and neighbor positions. In contrast with conventional center-based clustering, the PKM method traces several nearer clustering centers for a pixel i...

We examined the statistical performance of clustering single particle molecular images by bottom-up clustering, a hierarchical algorithm, using simulated protein images with a low signalto-noise ratio. Using covariance for the measure of similarity together with the iterative alignment, our method was found to be fairly robust against noise. Clustering tests of four known protein structures wer...