基于改进局部特征分布的图像分类方法

提出一种概率签名的图像分布描述及对应的图像分类算法．算法首先通过高斯混合模型建立图像局部特征分布，然后以混合模型中各个模式的均值为聚类中心，以图像中满足约束条件的局部特征对相应模式的后验概率之和为聚类大小来形成初始的概率签名，最后执行一个压缩过程确定最终的概率签名特征，并通过训练基于Earth Mover's Distance (EMD)核的SVM分类器完成图像分类．概率签名允许一个局部特征对多个聚类做出反映，可以编码更多判别信息以及从视觉感知上捕捉更多的相似性．通过与其它图像分类方法在场景识别和对象分类两项任务上的对比实验，验证了文中提出的分类方法的有效性．

Efficient Image Categorization Based on Improved Distributions of Local Features

An efficient image categorization method based on improved distribution of local invariant features is proposed. Firstly, the distribution of local features of an image is modeled by Gaussian mixed models (GMMs). Then, initial probability signatures are established by projecting local features onto all single models of GMMs. Finally, the complete probability signatures are obtained by performing a compression process. The probability signatures retain high discriminative power of probability density function (PDF) model, and they are suited for measuring dissimilarity of images with earth mover's distance (EMD), which allows for partial matches of compared distributions. The images are classified by learning support vector machine classifier with EMD kernel. The proposed method is evaluated on three image databases in scene recognition and image categorization tasks. The results of comparative experiments show that the proposed algorithm has inspiring performance.