Using graphical models to infer multiple visual classification features.
Using graphical models to infer multiple visual classification features.
复制标题
使用图形模型推断多个视觉分类特征。
DOI：
10.1167/9.3.23
复制
发表时间：
2009
影响因子：
1.8
通讯作者：
Cohen,AndrewL
中科院分区：
文献类型：
--
作者：
Ross,MichaelG;Cohen,AndrewL
本文描述了一种新的人类视觉分类模型，该模型能够恢复图像特征，从而解释不同视觉分类任务的性能。与一些常见的方法不同，该算法不能解释在原始图像像素上操作的单个线性分类器的性能。相反，它将分类建模为组合多个特征检测器的输出的结果。这种方法提取更多的信息，人类视觉分类比以前可能与其他方法，并提供了进一步探索的基础。
This paper describes a new model for human visual classification that enables the recovery of image features that explain performance on different visual classification tasks. Unlike some common methods, this algorithm does not explain performance with a single linear classifier operating on raw image pixels. Instead, it models classification as the result of combining the output of multiple feature detectors. This approach extracts more information about human visual classification than has been previously possible with other methods and provides a foundation for further exploration.