基于颜色的haar类特征的多类增强

Wen-Chung Chang, Chih-Wei Cho
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引用次数: 9

摘要

本文提出了一种基于颜色的类哈尔特征的多类增强算法。传统的多类提升算法基本上将多类问题视为两类问题的扩展。特别是,一旦目标类的数量增加,必须并行地扩展额外的强分类器。该方法的思想是开发一个能够解决多类问题的单一强分类器。为了使多类算法易于处理,要求系统选择一组能够正确分类多种类型目标的弱分类器。与基于灰度图像计算特征值的标准haar样特征相比,看似新颖的haar样特征需要基于彩色图像进行计算。由于从彩色图像空间到灰度图像空间的映射是一种外射,使用标准haar样特征的检测算法不可避免地忽略了原始彩色图像中可用的颜色信息。采用所提出的基于颜色的haar样特征的强分类器通常在检测和正确分类率方面具有相当的性能,与使用标准haar样特征的分类器相比,弱分类器较少。本文提出的增强算法可以提高系统效率,利用单个强分类器解决多类问题,而现有方法较为复杂,两类分类器数量较多。我们的方法已经在真实的交通环境中得到了成功的验证,通过安装在公路车辆上的CCD摄像机进行实验,其中目标被定义为乘用车和摩托车。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-class Boosting with Color-Based Haar-Like Features
This paper presents a multi-class boosting algorithm employing color-based Haar-like features. Traditional multi-class boosting algorithms basically regard multi-class problems as extensions of two-class problems. In particular, additional strong classifiers must be parallelly extended once the number of target classes increases. The idea in the proposed approach is to develop a single strong classifier which is capable of resolving multi-class problems. To make the multi-class algorithm tractable, the proposed system is required to select a set of weak classifiers which could classify multiple types of targets correctly. In contrast to standard Haar-like features that compute feature values based on gray level images, the seemingly novel Haar-like features require computation based on color images. Since the mapping from color image space to gray level image space is an epimorphism, detection algorithms using standard Haar-like features inevitably disregard color information available in original color images. Strong classifiers adopting the proposed color-based Haar-like features typically appear to have comparable performance, in the aspects of detection and correct classification rates, with fewer weak classifiers when compared with the one employing standard Haar-like features. The proposed boosting algorithm can improve system efficiency and resolve multi-class problems by a single strong classifier, whereas existing approaches are more complicated and the number of two-class classifiers could be relatively large. Our approach has been successfully validated in real traffic environments by performing experiments with a CCD camera mounted onboard a highway vehicle, where the targets are defined as passenger cars and motorcycles.
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