Proximity Clustering for Revealing a Semantically Dominant Class

We propose the proximity clustering (PxC) algorithm, which finds the spatially diverse but semantically similar data-point colonies in the form of a dominant class cluster. It searches through different proximity metric spaces for revealing the hidden porous data pattern. For each data-point, it provides the degree of belongingness to a dominant cluster and thereby performing the soft clustering. Performance evaluation on an artificial dataset, for finding a dominant class, shows that the PxC outperforms the other clustering methods. We experimentally validate its applications for image classification, segmentation and an abnormal event detection from the video using the proposed motion features. Experimental results show that the PxC improves performance of the image classification and the unsupervised image segmentation.