基于改进贝叶斯程序学习的物体形状分类

Object Shape Classification Based on Improved Bayesian Program Learning

为解决传统物体形状分类方法存在训练时间长以及形状描述不准确的问题,提出一种基于改进贝叶斯程序学习的图像分类方法。先将物体轮廓进行预处理并分割为长度固定的轮廓片段,使用形状描述符记录其形状信息,然后采用高斯混合模型对同一类物体的轮廓片段集训练出轮廓片段库,最后从测试图像的轮廓上均匀提取10个轮廓片段作为测试样本的解析,使用贝叶斯分类器计算样本解析与每类轮廓片段库中轮廓片段的拟合相似度,以其相似度值最高的类作为分类结果。在标准数据库Animal上的实验结果表明,本文方法具有较高的分类精度,同时大幅度缩短了训练时间。

In order to solve the problem that the traditional methods of object shape classification spend too much training time and the shape is represented inaccurately, an image classification method is proposed based on the improved Bayesian program learning. Firstly, the preprocessed object contours are segmented into fixed-length fragments and the feature information is represented with the shape descriptors. Then, the contour fragments in the same object class are trained into a contour fragment library using the Gaussian mixture model. Finally, the Bayesian classifier is used to calculate the similarity between the ten fragments of the test object and each contour fragment library, and the classification result is the category with the highest similarity value. The experimental results on standard Animal database show that the proposed method has a good classification accuracy, meanwhile, it greatly shortens the training time.