IEEE Trans Cybern. 2015 Dec;45(12):2905-13. doi: 10.1109/TCYB.2015.2389232. Epub 2015 Jan 22.
Recently, a large family of representation-based classification methods have been proposed and attracted great interest in pattern recognition and computer vision. This paper presents a general framework, termed as atomic representation-based classifier (ARC), to systematically unify many of them. By defining different atomic sets, most popular representation-based classifiers (RCs) follow ARC as special cases. Despite good performance, most RCs treat test samples separately and fail to consider the correlation between the test samples. In this paper, we develop a structural ARC (SARC) based on Bayesian analysis and generalizing a Markov random field-based multilevel logistic prior. The proposed SARC can utilize the structural information among the test data to further improve the performance of every RC belonging to the ARC framework. The experimental results on both synthetic and real-database demonstrate the effectiveness of the proposed framework.
最近，基于表示的分类方法的一个大家族已经被提出，并在模式识别和计算机视觉中引起了极大的兴趣。本文提出了一个通用框架，称为原子表示分类器（ARC），以系统地统一它们中的许多。通过定义不同的原子集，大多数流行的基于表示的分类器（RCs）都可以作为 ARC 的特例。尽管性能良好，但大多数 RCs 都分别对待测试样本，而没有考虑测试样本之间的相关性。在本文中，我们基于贝叶斯分析和推广基于马尔可夫随机场的多级逻辑先验，开发了一种结构 ARC（SARC）。所提出的 SARC 可以利用测试数据之间的结构信息，进一步提高属于 ARC 框架的每个 RC 的性能。在合成和真实数据库上的实验结果证明了所提出框架的有效性。
