Image segmentation algorithm of Gaussian mixture model based on map/reduce

Image segmentation has always been an important research direction in the field of images processing, however, due to the long cycle of algorithm, the image segmentation techniques have never been widely applied. According to the problem above, a image segmentation algorithm of Gaussian Mixture Model (GMM) based on Map/Reduce is proposed to improve the real-time performance. Firstly, the architecture of Hadoop image processing interface (Hipi) is presented to improve the ability of Map/Reduce to process small image. The K-means is present to generate the init value of Gaussian Mixture Model to avoid local optimal. Secondly, the cloud computational model of Map/Reduce is proposed to realize the parallel of GMM, and the fusion method of the sub-model for GMM is presented to keep the accuracy of segmentation. Finally, the experiment is carried out based on the cloud platform of aliyun. Results show the image segmentation algorithm of GMM based on Map/Reduce can greatly improve the real-time performance with keeping the accuracy of segmentation.