中文摘要：

图像表达是图像分类中最基本也是最重要的一个环节,当前的图像表达方法为了获得较高的分类性能,通常采用维度极高的特征向量.这给分类器的训练和特征的存储带来了极大的负担.同时,这些方法没有考虑图像的变化给图像表达所带来的影响.为此,针对以上的问题提出了一种对图像的可变性进行建模的方法.该方法首先使用高斯混合模型对底层视觉特征进行建模;再构造图像的充分统计量;最后采用可变性分析对充分统计量进行分解,并结合偏最小二乘回归方法获得紧致的图像表达.在公开的主流图像分类数据库上,该方法在获得更高的分类性能的同时极大地降低了分类器的训练和特征存储的开销.

英文摘要：

Image representation is the most fundamental and important aspect in image classification tasks. Most existing image representation methods use quite high dimensional feature vectors for image representation in order to achieve desired performance, which results in an inevitable drawback which is a classification problem with very high-dimensional feature vectors. Meanwhile, the existing methods have not considered image variations in image representation. Thus, an image representation method was proposed to model the variability in image classification. First, a Gaussian mixture model （GMM） was used to model the low-level visual feature vectors. Then, the sufficient statistics of images were constructed. Finally, the proposed variability analysis was utilized to decompose the sufficient statistics, and a compact image representation was obtained by means of partial least souure re~ro~inn ＂T＇ho n~nnnqoclmethod not only achieves better performance on the public image classification datasets, but also reduces the burdens of classifier training and feature storage.