基于相位奇点包的图像分类方法研究

相位奇点是复信号中的零点。在此前的研究中，我们发现相位奇点可提供丰富的图像信息，并对变换噪声等具有鲁棒性。本论文将相位奇点与视觉词袋模型相结合，构建图像表示的相位奇点包表示方法，并在此基础上开发图像的分类方法。与传统基于SIFT检测子的图像表示方法相比，基于相位奇点的表示方法不仅可以找到更多的兴趣点，而且可以根据相位奇点的正负属性进行单词的预分类。实验结果表明作者提出的方案是可行的，并在PASCAL2005图像分类的结果上表现的比基于SIFT的方法更好。

Image Classification Using Bag of Phase Singularities

Phase singularities (PSs) refer to the zero points in complex signals. In previous studies, we found that PSs can provide rich information for image representation and are robust to image transformation, noise etc. This paper makes use of PS and bag of visual words (BoVW) model to construct bag of PS representation for images. Then we use SVM to classify bag of PS representations. Compared with previous works using SIFT points, our new representations not only use more interested points, but also allow us to pre-classify the words according to the sign property of PSs. The experimental results show that the proposed methods achieve better performance on PASCAL2005 image classification tasks than SIFT detectors.