Description: This paper presents a probabilistic part-based approach for texture and object recognition. Textures are represented using a part dictionary found by quantizing the appearance
of scale- or affine-invariant keypoints. Object classes are represented using a dictionary of composite semi-local parts, or groups of neighboring keypoints with stable and
distinctive appearance and geometric layout. A discriminative
maximum entropy framework is used to learn the posterior
distribution of the class label given the occurrences
of parts from the dictionary in the training set. Experiments
on two texture and two object databases demonstrate the
effectiveness of this framework for visual classification.
- [detect] - Examples of motion detection, suitable f
- [facedetect] - Human Face Detection Algorithm VC implem
- [thomas-cvpr06] - Paper Towards Multi-View Object Class De
- [algorithm] - Th resho lding is an impo rtant fo rm of
- [zhangle] - Zhang Le maximum entropy toolkit command
- [TEST] - Face detection, eye detection, image seg
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