一种基于最大熵和部件的物体检测方法。在人脸检测上的应用取得了很大的准确率提升。-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.