• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International Conference Interdisciplinarity in Engineering >Gabor Feature Selection Based on Information Gain
【24h】

Gabor Feature Selection Based on Information Gain

机译：Gabor特征选择基于信息增益

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In the field of machine vision object detection has become a popular area over the past several years. It is applied on a large scale in scientific research such as bioinformatics, machine learning and computer vision or in everyday life, like traffic supervision, access control, identification and authentication systems and also in industry, robotics etc. Every application has its own particularities and works only in some well-defined conditions. The main difficulty of general object detection comes from the extreme diversity in which all objects appear. They have a large variety of appearance, aspect, form, dimension, color, position, rotation angle, illumination, shadow or occlusion. In this paper we use numerous Gabor filters for feature extraction, specially tuned for global face and local eye detection. Because the high dimensionality of the data the obtained features are hardly manageable. We propose to apply, in the training and test phases, feature selection. Feature selection is an important step in almost every data mining problem. The selection of the most representative feature-descriptors is done by measuring the pairwise entropy of the filter responses. The final classification result is given by the most informative filter responses obtained from information gain of a weak classifiers computed from the corresponding filter responses on the training set. Besides, this paper compares other learning methods used in our previous works with the currently proposed approach, comparing the role of measuring the information gain and the mutual information between the selected filters.
机译：在机器视野中，对象检测已经成为过去几年的流行区域。它在大规模的科学研究中应用，如生物信息学，机器学习和计算机视觉或日常生活中，如交通监督，访问控制，识别和认证系统，也是在工业，机器人等中的每个应用程序都有自己的特殊性和仅在一些明确的条件下工作。一般物体检测的主要难度来自所有对象出现的极端分集。它们具有各种各样的外观，方面，形式，尺寸，颜色，位置，旋转角度，照明，阴影或闭塞。在本文中，我们使用众多Gabor过滤器进行特征提取，专门调整全球面部和局部眼睛检测。因为数据的高维度，所获得的功能几乎不可扫描。我们建议在培训和测试阶段申请，功能选择。特征选择是几乎每个数据挖掘问题的重要步骤。通过测量滤波器响应的成对熵来完成最多代表性特征描述符的选择。最终的分类结果由从从训练集上的相应滤波器响应计算的弱分类器的信息增益获得的最具信息丰富的滤波器响应给出。此外，本文比较了我们以前的工作中使用的其他学习方法与当前提出的方法，比较了测量信息增益和所选过滤器之间的互信息的作用。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号