基于保真度加权判别协同竞争表示的鲁棒图像分类

ROBUST IMAGE CLASSIFICATION BASED ON FIDELITY WEIGHTED DISCRIMINANT CO-COMPETITIVE REPRESENTATION

  • 摘要: 为了深度挖掘类别之间的信息,提升方法鲁棒性和准确度,提出一种基于加权判别式协同竞争表示的鲁棒图像分类方法。该文将所有类之间的判别和竞争协作表示集成到统一模型中;在模型中引入两个判别约束和加权类别表示系数的约束,进一步提升类别对表征的贡献率;引入一种具有保真度的鲁棒算法,有效提升对噪声的鲁棒性。对6组图像数据集进行实验验证,结果证明提出的方法具有更高的分类精度与鲁棒性。

     

    Abstract: In order to deeply mine the information between categories and improve the robustness and accuracy of the method, a robust image classification method based on weighted discriminant cooperative competition representation is proposed. The discriminant and competition-collaboration representation between all classes was integrated in a unified model. Two discriminant constraints and the constraint of weighted class representation coefficient were introduced to further enhance the contribution rate of class to representation. Furthermore, a robust algorithm with fidelity was introduced to improve the robustness to noise. Experimental results on six sets of image data show that the proposed method has higher classification accuracy and robustness.

     

/

返回文章
返回