A new vision inspired clustering approach

Abstract

In this paper, a new clustering approach by simulating human vision process is presented. Human is good at detecting and segmenting objects from the background, even when these objects have not been seen before, which are clustering activities in fact. Since human vision shows good potential in clustering, it inspires us that reproducing the mechanism of human vision may be a good way of data clustering. Following this idea, we present a new clustering approach by reproducing the three functional levels of human vision. Numeric examples show that our approach is feasible, computationally stable, suitable to discover arbitrarily shaped clusters, and insensitive to noises. © 2013 Springer-Verlag.