Image segmentation is a main field of application in parallel computing.To achieve the high performance needed

the algorithm needs to make use of the improved computer hardware and parallel computing algorithms. Mean Shift algorithm is a relative classic algorithm in image segmentation field

which needs no prior knowledge and is an unsupervised segmentation process

attracting widespread attention for its good applicability. In this paper

we give two parallel improvement methods of Mean Shift using TBB(threading building block)and CUDA(compute unified device architecture)based on Multi-core and GPU(graphic processing unit) processing. First

the most time-consuming part the Mean Shift iteration in the process of Mean Shift image segmentation

is analyzed

then two parallel improvement methods of the Mean Shift iteration using TBB and CUDA are given

Two parallel methods are compared and analyzed. The experimental results show that

two kinds of parallel methods have achieved preferable acceleration effect

with the increase of the image and bandwidth parameter the speedup of two parallel methods is on the increases

and the speedup based on TBB tends to be equal to the number of CPUs.