Using multiple feature descriptors simultaneously increases the accuracy of object recognition. Usage of dynamic coefficients, in other words, non-identical and proportional to the importance of each descriptor for each class, is an appropriate and efficient way to combine different feature descriptors. In this paper, a new and efficient structure is proposed that calculates these coefficients using the sparse representation classifier. For each feature descriptor, we propose an important criterion based on the reconstruction error of the images via the sparse representation. The assigned importance of each descriptor for each class is different and calculated based on the reconstruction errors of the images when only their classmate images contribute in the reconstruction process. In addition, an innovative method is proposed which can be used to help classes that are not well described by any descriptor as well. In this method, using the residual criteria, these classes are identified and using a defined notion of similarity among classes, the accuracy of these classes with the support of the similar ones is enhanced. The experimental results of the proposed work on Caltech-101 and Caltech-256 databases show the success of approaches compared with state-of-the-art ones on the same databases.

中文翻译：

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使用稀疏表示分类器 (SRC) 计算多任务联合空间金字塔匹配的动态系数

同时使用多个特征描述符可以提高对象识别的准确性。动态系数的使用，换句话说，不相同且与每个类的每个描述符的重要性成正比，是组合不同特征描述符的适当且有效的方法。在本文中，提出了一种新的高效结构，使用稀疏表示分类器计算这些系数。对于每个特征描述符，我们通过稀疏表示基于图像的重建误差提出了一个重要的标准。每个类的每个描述符的分配重要性是不同的，并且当只有他们的同学图像参与重建过程时，根据图像的重建误差计算。此外，提出了一种创新方法，可用于帮助任何描述符都不能很好描述的类。在该方法中，使用残差标准来识别这些类，并使用定义的类间相似性概念，在相似类的支持下提高了这些类的准确性。Caltech-101 和 Caltech-256 数据库上拟议工作的实验结果表明，与相同数据库上最先进的方法相比，这些方法是成功的。