A novel mean-shift architecture for scalable multiprocessor implementation

Abstract

Organizing data into its natural grouping based on intrinsic characteristics is the most sensible thing to do with unlabeled data. Mean shift is a non-parametric mode seeking algorithm widely used for data clustering, image segmentation and object tracking, but its use in real time applications is limited because of its high computational cost. In this paper we propose a hybrid, sequentially unfolded, model for the implementation of mean shift clustering algorithm targeting Hardware Platforms. The proposed model uses multiple processors working in parallel on independent data allowing faster convergence of the algorithm. The model also supports scalability with respect to total number of working processors. Experimentation has yielded that computational cost decreases exponentially in each iteration. The proposed model can be implemented on any hardware which can support parallel architecture. Finally, it is also shown that hardware implementation will give same results as its software implementation.

Keywords Clustering algorithms, Computational modeling, Hardware, Real-time systems, Field programmable gate arrays