Learning coupled conditional random field for image decomposition with application on object categorization

This paper proposes a computational system of object categorization based on decomposition and adaptive fusion of visual information. A coupled Conditional Random Field is developed to model the interaction between low level cues of contour and texture, and to decompose contour and texture in natural images. The advantages of using coupled rather than single-layer Random Fields are demonstrated with model learning and evaluation. Multiple decomposed visual cues are adaptively combined for object categorization to fully leverage different discriminative cues for different classes. Experimental results show that the proposed computational model of “recognition-through-decomposition-and-fusion” achieves better performance than most of the state-of-the-art methods, especially when only a limited number of training samples are available.