中文摘要：

稀疏编码在编码过程中忽略特征之间的局部关系，使编码不稳定，并且优化问题中的减法运算可能会导致特征之间相互抵消．针对上述2个问题，文中提出融合局部性和非负性的Laplacian稀疏编码的图像分类方法．引入局部特征附近的基约束编码，利用非负矩阵分解将非负性加到Laplacian稀疏编码中，利用空间金字塔划分和最大值融合表示最终的图像，并采用多类线性SVM分类图像．本文方法保留特征之间的局部信息，避免特征之间相互抵消，保留更多的特征，从而改善编码的不稳定性．在4个公共数据集上的实验表明，相比其它现有算法，本文方法分类准确率更高．

英文摘要：

proposed, named Laplacian sparse coding by incorporating locality and non-negativity （LN-LSC）for image classification. Firstly, bases near to the local features are chosen to constrain the codes. Then, non-negativity is introduced in Laplacian sparse coding by non-negative matrix factorization. Finally, spatial pyramid division and max pooling are utilized to generate the final representation of images in the pooling step. Multi-class linear SVM is adopted for image classification. The local information betweenfeatures is preserved by the proposed method, and the offsetting between features is also avoided. Thus, more features are utilized for coding, and the instability of the coding is overcome. Experiments on four public image datasets show the classification accuracy of LN-LSC is higher than that of the state-of-the-art sparse coding algorithms.