Image Segmentation Based on Kernel Clustering in Compressed Domain

A new method of image segmentation in compressed domain is proposed in this paper. Firstly, DCT coefficients of sub-block are extracted directly from DCT coefficients of macro-blocks to avoid IDCT. Secondly, random Fourier maps are used to accelerate kernel clustering. The key idea behind the use of random Fourier maps for clustering is to project the data into a low-dimensional space where the inner product of the transformed data points approximates the kernel similarity between them. An efficient linear clustering algorithm can then be applied to the points in the transformed space, and then the DCT coefficients with sub-block can be treated as input data for kernel clustering.