中文摘要：

目的稀疏编码是图像特征表示的有效方法，但不足之处是编码不稳定，即相似的特征可能会被编码成不同的码字。且在现有的图像分类方法中，图像特征表示和图像分类是相互独立的过程，提取的图像特征并没有有效保留图像特征之间的语义联系。针对这两个问题，提出非负局部Laplacian稀疏编码和上下文信息的图像分类算法。方法图像特征表示包含两个阶段，第一阶段利用非负局部的Laplacian稀疏编码方法对局部特征进行编码，并通过最大值融合得到原始的图像表示，从而有效改善编码的不稳定性；第二阶段在所有图像特征表示中随机选择部分图像生成基于上下文信息的联合空间，并通过分类器将图像映射到这些空间中，将映射后的特征表示作为最终的图像表示，使得图像特征之间的上下文信息更多地被保留。结果在4个公共的图像数据集Corel．10、Scene-15、Caltech-101以及Cahech-256上进行仿真实验，并和目前与稀疏编码相关的算法进行实验对比，分类准确率提高了约3％～18％。结论本文提出的非负局部Laplacian稀疏编码和上下文信息的图像分类算法，改善了编码的不稳定性并保留了特征之间的相互依赖性。实验结果表明，该算法与现有算法相比的分类效果更好。另外，该方法也适用于图像分割、标注以及检索等计算机视觉领域的应用。

英文摘要：

Objective Image classification is an important issue in computer vision and a hot research topic. The traditional sparse coding （SC） method is effective for image representation and has achieved good results in image classification. How- ever, the SC method has two drawbacks. First, the method ignores the local relationship between image features, thus los- ing local information. Second, because the combinatorial optimization problems of SC involve addition and subtraction, the subtraction operation might cause features to be cancelled. These two drawbacks result in coding instability, which means similar features are encoded into different codes. Meanwhile, representation and classification are usually independent of each other during image classification, so the features of image semantic relations between image features are not well preserved. In other words, image representation is not task-driven and may be unable to perform the final classification task well. Furthermore, the local feature quantization method disregards the underlying semantic information of the local region, which influences the classification performance. To deal with such problems, a two-stage method of image classification with non-negative and local Laplacian SC and context information （NLLSC-CI） is proposed in this study. NLLSC-CI aims to improve the efficiency of image representation and the accuracy of image classification. Method The representation of an image involves two stages. In the first stage, non-negative and locality-constrained Laplacian SC （NLLSC） is introduced to the encoding of the local features of the image to overcome coding instability. First, non-negativity is introduced in Lapla- cian SC （LSC） by non-negative matrix factorizafion （NMF） to avoid offsetting between features, which is applied to con- strain the negativity of the codebook and code coefficient. Second, bases that are near the local features are selected to con- strain the codes because locality is more important than sparseness; th