Mixtures of Trees for Object Recognition

Efficient detection of objects in images is complicated by variations of object appearance due to intra-class object differences, articulation, lighting, occlusions, and aspect variations. To reduce the search required for detection, we employ the bottom-up approach where we find candidate image features and associate some of them with parts of the object model. We represent objects as collections of local features, and would like to allow any of them to be absent, with only a small subset sufficient for detection; furthermore, our model should allow efficient correspondence search. We propose a model, Mixture of Trees, that achieves these goals. With a mixture of trees, we can model the individual appearances of the features, relationships among them, and the aspect, and handle occlusions. Independences captured in the model make efficient inference possible. In our earlier work, we have shown that mixtures of trees can be used to model objects with a natural tree structure, in the c...

• Type: Conference
• Year: 2001
• Where: CVPR
• Authors: Sergey Ioffe, David A. Forsyth
• Keywords: Computer Vision, CVPR 2001, Efficient Correspondence Search, Efficient Detection, Natural Tree Structure, Object Model, View-invariant Face Detection