基于图变换的图像压缩采样与分类

提出一种基于图论表示的正交变换基,并在此基础上对图像进行压缩采样与压缩域直接分类.首先,充分利用图像的边缘特性和像素关系,给出一种图像的图论表示方法;然后,通过图Laplacian矩阵的特征值分解得到其特征向量矩阵作为正交变换基,由此得到图像的图变换域稀疏表示;最后,利用随机投影后的压缩采样特征向量直接对分类器进行训练和测试,不仅保持了与原空间相当的分类精度,还大量地减少了训练和测试时间以及计算/存储代价.

Graph-transform based image compressive sampling and classification

For compressive sampling and classify of images, an orthogonal transform basis based on graph presentation is proposed. Firstly, according to correlation of both edges and pixels, an improved graph presentation of an image is introduced. Then the orthogonal transform basis is constructed as the eigenvectors matrix after eigenvalue decomposition of the Laplacian of the graph, based on which the sparse representation of images is obtained. Finaiiy, the random projection of compressed features is used to train and test the classifier directly in the compressed domain. The proposed method has a similar classifying performance in the original domain with abundantly reduced training and testing time, as well as computational/store cost.