Texture synthesis based on cluster transition probabilities

Abstract

This paper introduces an approach for synthesizing natural textures. Textures are modeled using a block-transition probabilistic model. In the training phase, the original textured image is split into equal size blocks, and clustered using the k-means clustering algorithm. Then, the transition probabilities between block-clusters are calculated. In the synthesis phase, the algorithm generates a sequence of indices, each representing a block-cluster, based on the transition probabilities. One advantage of this method over previous block sampling techniques is its stability. More specifically, the texture is synthesized block-by-block in a raster order. The block at a specific location is selected from one of the original image blocks. Thus, synthesis does not lead to artifacts. Additionally, the algorithm uses pre-and postfiltering. The image is filtered by a predictive filter, and the residual image is modeled using the probabilistic approach. The final synthesized image is the result of filtering the residual image by the inverse filter. Using pre-and postprocessing eliminates the blockage effect. Moreover, the algorithm is computationally inexpensive, and the synthesis phase is particularly fast since it only requires generation of a sequence of cluster indices. Results show that the proposed method is successful in synthesizing realistic natural textures for a large variety of textures.

Texture synthesis: textons revisited

“…T EXTURE classification, segmentation [1], [3], and synthesis [4]- [23], [26]- [30] find important applications in computer and machine vision. In particular, texture synthesis applications include computer graphics, image modeling, camouflaging, and in general, generation of visually realistic natural environments.…”