A novel image classification method based on sparse coding and multiple kernel learning is proposed in the paper.Traditional methods of image classification used common sparse coding but lose the spatial information.We add this spatial information by dividing the image with the spatial pyramid.With the nonlinear SVM for image classification,each level of spatial pyramid has its own kernel,and we adopt machine learning for the optimal trade-off between different kernels.A much more discriminative kernel can be seen as the linear combination of base kernels corresponding to different pyramid levels.The experiments on the benchmark dataset show the effectiveness and robustness of our method.The precision on scene categories dataset can reach 83.10%,and it is the best result comparing to the state-of-the-art work.%本文提出一种基于稀疏编码的多核学习图像分类方法.传统稀疏编码方法对图像进行分类时,损失了空间信息,本文采用对图像进行空间金字塔多划分方式为特征加入空间信息限制.在利用非线性SVM方法进行图像分类时,空间金字塔的各层分别形成一个核矩阵,本文使用多核学习方法求解各个核矩阵的权重,通过核矩阵的线性组合来获取能够对整个分类集区分能力最强的核矩阵.实验结果表明了本文所提出图像分类方法的有效性和鲁棒性.对Scene Categories场景数据集可以达到83.10％的分类准确率,这是当前该数据集上能达到的最高分类准确率.
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