Primal sketch: Integrating structure and texture

This article proposes a generative image model, which is called ‘‘primal sketch,’’ following Marr’s insight and terminology. This model combines two prominent classes of generative models, namely, sparse coding model and Markov random field model, for representing geometric structures and stochastic textures, respectively. Specifically, the image lattice is divided into structure domain and texture domain. The sparse coding model is used to represent image intensities on the structure domain, where edge and ridge segments are modeled by image coding functions with explicit geometric and photometric parameters. The edge and ridge segments form a sketch graph whose nodes are corners and junctions. The sketch graph is governed by a simple spatial prior model. The Markov random field model is used to summarize image intensities on the texture domain, where the texture patterns are characterized by feature statistics in the form of marginal histograms of responses from a set o...

• Type: Journal
• Year: 2007
• Where: CVIU
• Authors: Cheng-en Guo, Song Chun Zhu, Ying Nian Wu
• Keywords: CVIU 2007, Markov Random, Model, Texture Domain