基于核拉普拉斯稀疏编码的图像分类

使用稀疏编码解决计算机视觉问题可以取得良好的效果.然而,以往的稀疏编码都是在原始特征空间进行.受核方法可以获得特征的高维非线性映射的启发,扩展了拉普拉斯稀疏编码(LSc),提出了核拉普拉斯稀疏编码(KLSc),它可以降低特征量化误差,增强稀疏编码的性能.在3个标准数据集上的实验结果表明,所提出的基于KLSc的图像分类算法具有良好的分类效果,分类正确率优于LSc.

Kernel Laplacian sparse coding for image classification

Sparse coding can achieve good performance in some computer vision problems. However, past sparse coding was implemented in the original feature space. Kernel method can acquire high dimensional nonlinear mapping characteristics. Inspired by it, the Laplacian sparse coding (LSc) is extended, and the kernel Laplacian sparse coding (KLSc) is proposed. It can reduce the feature quantization error and enhance the sparse coding performance. Experimental results of three standard datasets show that the proposed image classification algorithm based on KLSc has good classification effect, and the correct classification rate is better than that of LSc method.