Unsupervised image object segmentation over compressed domain

Direct processing of JPEG images based on DCT coefficients could avoid computationally intensive full decoding and large memory storage. In this paper, we exploit the inherent information extracted from DCT coefficients to achieve unsupervised segmentation of image objects. First, a maximum entropy fuzzy clustering (MEFC) algorithm is proposed to achieve a coarse segmentation based on DCT-DC coefficients. The DCT-AC coefficients are then utilized to refine the segmentation boundary by a maximum a posteriori (MAP) approach. The major challenge of the problem is to achieve satisfactory segmentation simply based on DCT coefficients, which are quantized and coarse information in essence. Experimental results show the promising potential of the proposed algorithm in overcoming these fundamental limitations.