Department of Statistics, UCLA

Following Marr’s insight, we propose a generative image representation called primal sketch, which integrates two modeling components. The first component explains the structural part of an image, such as ob ject boundaries, by a hidden layer of image primitives. The second component models the remaining textural part without distinguishable elements by Markov random fields that interpolate the structural part of the image. We adopt an artist’s notion by calling the two components “sketchable” and “non-sketchable” parts respectively. A dictionary of image primitives are used for modeling structures in natural images, and each primitive is specified by variables for its photometric, geometric, and topological attributes. The primitives in the image representation are not independent but organized in an sketch graph. This sketch graph is modeled by a spatial Markov model that enforces Gestalt organizations. The inference of the sketch graph consists of two phases. Phase I sequentially adds the most prominent image primitives in a procedure similar to matching pursuit. Phase II edits the sketch graph by a number of graph operators to achieve good Gestalt organizations. Experiments show that the primal sketch model produces satisfactory results for a large number of generic images. The primal sketch model is not only a parsimonious image representation for lossy image coding, but also provides a meaningful mid-level generic representation for other vision tasks.