Image Segmentation via Online Encoding Based Spectral Clustering
Ning Liu · Journal of Chinese Computer Systems · 2013
Currently,spectral clustering is a state-of-art technique in image segmentation.However,the O(n3) complexity of spectral clustering its application in image segmentation.Based on the online multiscale compeittive learning,this paper proposes a new rapid spectral clustering algorithm for segmenting of images.This algorithm uses m(mn) constructed prototypes by online competitive learning to approximate the distribution of data and then groups prototypes by multiscale spectral clustering.With the approximate complexity O(mn+m2),our algorithm shows high performance and segmentation quality for large scale images.Our algorithm is tested on three data sets.On first data set,our algorithm shows correct grouping of data while NJW algorithm does not.Second,we compute the time consumption of our algorithm and NJW,and present the compression ratios in our algorithm.Results have shown our algorithm behavior better than NJW.At last the segmentation results on standard images of our algorithm take advantage of NJW and Kmeans algorithms and sampling based Nystrm grouping method.