非负局部约束线性编码图像分类算法

刘培娜; 刘国军; 郭茂祖; 刘扬; 李盼

doi:10.16383/j.aas.2015.c140753

非负局部约束线性编码图像分类算法

doi: 10.16383/j.aas.2015.c140753

1.
哈尔滨工业大学计算机科学与技术学院哈尔滨 150001

基金项目:

国家自然科学基金 (61171185, 61271346), 黑龙江省青年科学基金 (QC2014C071)资助

详细信息

作者简介:
刘培娜哈尔滨工业大学计算机科学与技术学院硕士研究生. 2013 年获得内蒙古大学计算机学院学士学位. 主要研究方向为计算机视觉与机器学习.E-mail: liupeina@hit.edu.cn

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出版历程
- 收稿日期: 2014-10-28
- 修回日期: 2015-03-03
- 刊出日期: 2015-07-20

Image Classification Based on Non-negative Locality-constrained Linear Coding

1.
School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001

Funds:

Supported by National Natural Science Foundation of China (61171185, 61271346) and Heilongjiang Province Science Foundation for Youths (QC2014C071)

摘要

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.
- 局部约束线性编码 /
- 非负约束 /
- 空间金字塔匹配 /
- 图像分类
Abstract: 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 benchmarks, 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.
- Locality-constrained linear coding (LLC) /
- non-negative constraint /
- spatial pyramid matching (SPM) /
- image classification

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