Feature co-occurrence representation based on boosting for object detection
Feature co-occurrence representation based on boosting for object detection
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DOI：
10.1109/cvprw.2010.5543173
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发表时间：
2010-06
期刊：
影响因子：
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通讯作者：
Yuji Yamauchi;M. Takagi;Takayoshi Yamashita;H. Fujiyoshi
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文献类型：
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作者：
Yuji Yamauchi;M. Takagi;Takayoshi Yamashita;H. Fujiyoshi
提出了一种基于Boosting的特征共现表示方法，用于目标检测。AdaBoost先前提出的一种结合多个二进制分类代码来表示特征共现的方法已经被证明在人脸检测中是有效的。然而，如果输入特征由于遮挡或其他因素而难以被分配给正确的二进制代码，则这里出现问题，因为二进制分类和共现表示的过程可能组合包括错误代码的特征。针对这一问题，提出了一种共生概率特征(CPF)，该特征利用Real AdaBoost的加法和乘法算子将多个弱分类器组合在一起，其中弱分类器的输出是实数。由于CPF使用两种类型的算子将分类器组合在一起，因此可以表示不同类型的共现，并且可以预期提高检测性能。为了表示更多样化的同现，本文还提出了一种应用减法运算的同现表示。尽管使用加法和乘法运算符的同现表示可以表示特征之间的同现，但是使用减法运算符使得能够表示局部特征和具有其他属性的特征之间的同现。这应该具有修正从局部特征获得的检测目标类别的概率的效果。评估实验表明，该方法的共现表示是有效的。
This paper proposes a method of feature co-occurrence representation based on boosting for object detection. A previously proposed method that combines multiple binary-classified codes by AdaBoost to represent the co-occurrence of features has been shown to be effective in face detection. However, if an input feature is difficult to be assigned to a correct binary code due to occlusion or other factors, a problem arises here since the process of binary classification and co-occurrence representation may combine features that include an erroneous code. In response to this problem, this paper proposes a Co-occurrence Probability Feature (CPF) that combines multiple weak classifiers by addition and multiplication arithmetic operators using Real AdaBoost in which the outputs of weak classifiers are real values. Since CPF combines classifiers using two types of operators, diverse types of co-occurrence can be represented and improved detection performance can be expected. To represent even more diversified co-occurrence, this paper also proposes co-occurrence representation that applies a subtraction arithmetic operator. Although co-occurrence representation using addition and multiplication operators can represent co-occurrence between features, use of the subtraction operator enables the representation of co-occurrence between local features and features having other properties. This should have the effect of revising the probability of the detection-target class obtained from local features. Evaluation experiments have shown co-occurrence representation by the proposed methods to be effective.