中文摘要：

基于特征提取的图像分类算法的核心问题是如何对特征进行有效编码。局部约束线性编码（Locality-constrained linear coding,LLC）因其良好的特征重构性与局部平滑稀疏性,已取得了很好的分类性能。然而,LLC编码的分类性能对编码过程中的近邻数k的大小比较敏感,随着k的增大,编码中的某些负值元素与正值元素的差值绝对值也可能增大,这使得LLC越来越不稳定。本文通过在LLC优化模型的目标方程中引入非负约束,提出了一种新型编码方式,称为非负局部约束线性编码（Non-negative locality-constrained linear coding,NNLLC）。该模型一般采取迭代优化算法进行求解,但其计算复杂度较大。因此,本文提出两种近似非负编码算法,其编码速度与LLC一样快速。实验结果表明,在多个广泛使用的图像数据集上,相比于LLC,NNLLC编码方式不仅在分类精确率上提高了近1%～4%,而且对k的选取具有更强的鲁棒性。

英文摘要：

The most important issue of image classification algorithm based on feature extraction is how to efficiently encode features. Locality-constrained linear coding （LLC） has achieved the state-of-the-art performance on several bench- marks, due to its underlying properties of better construction and local smooth sparsity. However, the performance of LLC on image classification is sensitive to the number of neighbors, i.e., the value of k. With the increase of k, the absolute difference of some negative and positive elements may likely become larger and larger. This will make LLC more unstable. In this paper, a new coding scheme called non-negative locality-constrained linear coding （NNLLC） is proposed. It adds an extra non-negative constraint to the objective function of LLC. Generally, this new model can be solved by iterative optimization methods, however, such solutions are quite impractical due to high computational cost. Therefore, two fast approximation algorithms are proposed; more importantly, they and LLC have a similar computational complexity. To compare with LLC, the experiment results on several widely used image datasets demonstrate that NNLLC not only can improve the classification accuracy by nearly 1%～ 4 %, but also is more robust on the selection of k.