文件名称:zuidashang
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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 fr a mework 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 fr a mework for visual classification.
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 fr a mework 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 fr a mework for visual classification.
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