用于图像分类的通用特征选择

Pedro A. Rodriguez, Nathan G. Drenkow, D. DeMenthon, Zachary H. Koterba, Kathleen Kauffman, Duane C. Cornish, Bart Paulhamus, R. J. Vogelstein
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引用次数: 3

摘要

神经模拟算法,如HMAX算法,在图像分类任务中已经非常成功。然而,目前这些算法的实现不能很好地扩展到大型数据集。通常,目标特定的特征或补丁是提前“学习”的,然后在特征提取期间与测试图像相关联。在本文中,我们开发了一种新的方法来选择一组通用特征,使分类能够跨越广泛的图像类别。我们的方法使用一个大的特征字典来训练多个随机森林分类器,然后使用多数投票方案将它们组合起来。这使得基于特征重要性度量选择最具鉴别性的补丁成为可能。实验证明了该方法使用HMAX特征以及通用特征数量、分类性能和处理时间之间的权衡的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Selection of universal features for image classification
Neuromimetic algorithms, such as the HMAX algorithm, have been very successful in image classification tasks. However, current implementations of these algorithms do not scale well to large datasets. Often, target-specific features or patches are “learned” ahead of time and then correlated with test images during feature extraction. In this paper, we develop a novel method for selecting a single set of universal features that enables classification across a broad range of image classes. Our method trains multiple Random Forest classifiers using a large dictionary of features and then combines them using a majority voting scheme. This enables the selection of the most discriminative patches based on feature importance measures. Experiments demonstrate the viability of this method using HMAX features as well as the tradeoff between the number of universal features, classification performance, and processing time.
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