Image segmentation using linked mean-shift vectors for SIMD architecture

Hanjoo Cho, Sung In Cho, Young Hwan Kim

Abstract: This paper presents a new mean-shift based segmentation algorithm for single instruction multiple data (SIMD) architecture. A standard mean-shift algorithm has different number of computations for each pixel because the endpoints of each pixel's iteration process are different, thus a standard mean-shift algorithm is hard to be accelerated using SIMD architecture. The proposed algorithm, however, equalizes the number of computations for each pixel by constructing links between pixels using their first mean-shift vectors without iteration process. It makes the proposed algorithm more suitable for the SIMD architecture without a complicated scheduling module. Experimental results using the Berkeley segmentation dataset show the proposed algorithm successfully equalizes the number of computations with reasonable segmentation quality.