Texture synthesis based on multiple seed-blocks and support vector machines
Junyu Dong, Ran Wang, Xinghui Dong · 2010 3rd International Congress on Image and Signal Processing · 2010
We introduce a new method for texture synthesis based on multiple seed-blocks and support vector machines (SVM). First the sample texture is used to train the SVM model with class labels assigned to gray levels. During the synthesis process, each time we generate one patch in the left-to-right order in the result texture. The size of each patch is smaller than that of the sample, and we search a seed-block in the already generated patches to ensure the synthesized patch has similar texture characteristics as the sample. Support vector machines are used to generate pixel values within each patch. The advantage of using SVM is that the sample is not required during the synthesis stage since it has been modeled by a linear model. Unlike previous work in, which can only synthesize highly structured texture, the proposed method can successfully synthesize both random and structured textures. It is also extended to synthesize 3D surface texture or Bidirectional Texture Functions (BTF).