Image segmentation based on situational DCT descriptors
It is of utmost importance in multimedia processing to achieve still image segmentation, i.e., to partition images into regions of coherent color and texture. In this paper we propose a novel image segmentation method using a special visual descriptor. For each pixel p, the discrete cosine transform (DCT) of the block centered on p together with its location in the image is employed as its content descriptor thus resulting in a long vector , referred to as situational DCT descriptors (SDDs). A scalar quantization step is then carried out on the DCT component of SDDs to reflect the fact that the human vision system is not of uniform discriminative sensitivity to details of different frequencies. Next the principal component analysis is conducted to drastically reduce the dimensionality of SDDs. The adaptive K-means algorithm is then performed to arrive at the region assignment for each pixel. The final partitioning results are obtained after performing the post-processing step. Encouraging empirical performance has been demonstrated.
image segmentation dct k-means clustering