学习用于复杂对象分类的非冗余码本Learning Non-Redundant Codebooks for Classifying Complex Objects |
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课程网址: | http://videolectures.net/icml09_zhang_lnrccco/ |
主讲教师: | Wei Zhang |
开课单位: | 惠普公司 |
开课时间: | 2009-08-26 |
课程语种: | 英语 |
中文简介: | 基于码本的表示被广泛用于复杂对象(例如图像和文档)的分类。大多数先前基于码本的方法通过聚类构建单个码本,其将一包低级特征映射到描述这些特征的分布的固定长度直方图。本文描述了一个简单而有效的学习多个非冗余码本的框架,它产生了令人惊讶的好结果。在该框架中,按顺序学习每个码本以提取前面的码本及其相应的分类器未捕获的判别信息。我们将此框架应用于两个应用程序域:可视对象分类和文档分类。与单个码本或以套袋方式学习的码本相比,大型分类任务的实验显示出性能的显着改善。 |
课程简介: | Codebook-based representations are widely employed in the classification of complex objects such as images and documents. Most previous codebook-based methods construct a single codebook via clustering that maps a bag of low-level features into a fixed-length histogram that describes the distribution of these features. This paper describes a simple yet effective framework for learning multiple non-redundant codebooks that produces surprisingly good results. In this framework, each codebook is learned in sequence to extract discriminative information that was not captured by preceding codebooks and their corresponding classifiers. We apply this framework to two application domains: visual object categorization and document classification. Experiments on large classification tasks show substantial improvements in performance compared to a single codebook or codebooks learned in a bagging style. |
关 键 词: | 可视对象分类; 文档分类; 套袋方式 |
课程来源: | 视频讲座网 |
最后编审: | 2020-09-17:chenxin |
阅读次数: | 36 |