基于特征词及形状模型的图像类别学习

Image Category Learning Based on Feature Words and Shape Model

一类图像的特征及其分布在很大程度上表达了该类的主要信息.根据这一思想,结合图像中的像素信息及形状信息提出一种类图像识别方法.对于一类给定的样本图像,首先提取每一幅图像的显著特征,根据特征分布提取特征区域;然后对所有的特征区域进行聚类得到特征词典,基于特征词及形状信息建模,同时采用最大似然估计的方法进行学习得到模型参数;最后结合特征词模型及形状模型对测试图像进行识别.实验结果表明,该方法能够有效地对2类图像进行分类和识别,同时对多数类图像也能进行较为准确的分类和识别.

The features and distribution of one class of image largely represent their class information. In this paper, integrated with pixel information and shape information, we propose a method for image category recognition. The algorithm uses the following steps. First, given a specified image category, extract the salient features of each image, and then abstract the feature regions using the distribution of salient features. Second, aiming at get the feature dictionary, we cluster the feature regions. Based on feature words and shape information, we build the model, meanwhile we use the maximum likelihood estimation to learn the model parameters. Finally, by combing feature words model and shape model, test images are recognized. Experimental results show that our proposed method can categorize and recognize two classes of image effectively. The proposed method can also use for multiple classes of image.