PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Obj Cut
Mudigonda Pawan Kumar, Philip Torr and Andrew Zisserman
In: CVPR 2005, 22-24 June 2005, San Diego, USA.


In this paper we present a principled Bayesian method for detecting and segmenting instances of a particular object category within an image, providing a coherent methodology for combining top down and bottom up cues. The work draws together two powerful formulations: pictorial structures (PS) and Markov random fields (MRFs) both of which have efficient algorithms for their solution. The resulting combination, which we call the Object Category Specific MRF, suggests a solution to the problem that has long dogged MRFs namely that they provide a poor prior for specific shapes. In contrast, our model provides a prior that is global across the image plane using the PS. We develop an efficient method, OBJ CUT, to obtain segmentations using this model. Novel aspects of this method include an efficient algorithm for sampling the PS model, and the observation that the expected log likelihood of the model can be increased by a single graph cut. Results are presented on two object categories, cows and horses. We compare our methods to the state of the art in object category specific image segmentation and demonstrate significant improvements.

EPrint Type:Conference or Workshop Item (Oral)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:981
Deposited By:Mudigonda Pawan Kumar
Deposited On:13 June 2005