判别式
模式识别(心理学)
人工智能
范畴变量
计算机科学
稳健性(进化)
分类器(UML)
上下文图像分类
回归
机器学习
图像(数学)
数学
统计
生物化学
基因
化学
作者
Chong Peng,Yang Liu,Xin Zhang,Zhao Kang,Yongyong Chen,Chenglizhao Chen,Qiang Cheng
标识
DOI:10.1016/j.knosys.2021.107517
摘要
We introduce a new classifier for small-sample image data based on a two-dimensional discriminative regression approach. For a test example, our method estimates a discriminative representation from training examples, which accounts for discriminativeness between classes and enables accurate derivation of categorical information. Unlike existing methods that vectored image data, the learning of the representation in our method is performed with the two-dimensional features of the data, and thus inherent spatial information of the data is fully exploited. This new type of two-dimensional discriminative regression, different from existing regression models, allows for building a highly effective and robust classifier for image data through explicitly incorporating discriminative information and inherent spatial information. We compare our method with several state-of-the-art classifiers of small-sample images and experimental results show superior performance of the proposed method in classification accuracy as well as robustness to noise corruption.
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