基于弹性网和直方图相交的非负局部稀疏编码

Non-negative local sparse coding algorithm based on elastic net and histogram intersection

针对稀疏编码模型在字典基的选择时忽略了群效应,且欧氏距离不能有效度量特征与字典基之间距离的问题,提出基于弹性网和直方图相交的非负局部稀疏编码方法(EH-NLSC)。首先,在优化函数中引入弹性网模型,消除字典基选择数目的限制,能够选择多组相关特征而排除冗余特征,提高了编码的判别性和有效性。然后,在局部性约束中引入直方图相交,重新定义特征与字典基之间的距离,确保相似的特征可以共享其局部的基。最后采用多类线性支持向量机进行分类。在4个公共数据集上的实验结果表明,与局部线性约束的编码算法(LLC)和基于非负弹性网的稀疏编码算法(NENSC)相比,EH-NLSC的分类准确率分别平均提升了10个百分点和9个百分点,充分体现了其在图像表示和分类中的有效性。

To solve the problems that group effect is neglected when selecting dictionary bases in sparse coding models,and distance between a features and a dictionary base can not be effectively measured by Euclidean distance,Non-negative Local Sparse Coding algorithm based on Elastic net and Histogram intersection(EH-NLSC)was proposed.Firstly,with elastic-net model introduced in the optimization function to remove the restriction on selected number of dictionary bases,multiple groups of correlation features were selected and redundant features were eliminated,improving the discriminability and effectiveness of the coding.Then,histogram intersection was introduced in the locality constraint of the coding,and the distance between the feature and the dictionary base was redefined to ensure that similar features share their local bases.Finally,multi-class linear Support Vector Machine(SVM)was adopted to realize image classification.The experimental results on four public datasets show that compared with LLC(Locality-constrained Linear Coding for image classification)and NENSC(Non-negative Elastic Net Sparse Coding),the classification accuracy of EH-NLSC is increased by 10 percentage points and 9 percentage points respectively on average,proving its effectiveness in image representation and classification.