Statistical Modeling of Texture Sketch

Recent results on sparse coding and independent component analysis suggest that human vision first represents a visual image by a linear superposition of a relatively small number of localized, elongate, oriented image bases. With this representation, the sketch of an image consists of the locations, orientations, and elongations of the image bases, and the sketch can be visually illustrated by depicting each image base by a linelet of the same length and orientation. Built on the insight of sparse and independent component analysis, we propose a two-level generative model for textures. At the bottom-level, the texture image is represented by a linear superposition of image bases. At the top-level, a Markov model is assumed for the placement of the image bases or the sketch, and the model is characterized by a set of simple geometrical feature statistics.

• Type: Conference
• Year: 2002
• Where: ECCV
• Authors: Ying Nian Wu, Song Chun Zhu, Cheng-en Guo
• Keywords: Computer Vision, ECCV 2002, Image Bases, Independent Component Analysis, Linear Superposition, Oriented Image Bases, Texture Image