...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Transactions on Pattern Analysis and Machine Intelligence >Jointly Learning Visually Correlated Dictionaries for Large-Scale Visual Recognition Applications
【24h】

Jointly Learning Visually Correlated Dictionaries for Large-Scale Visual Recognition Applications

机译：联合学习用于大型视觉识别应用程序的视觉相关词典

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Learning discriminative dictionaries for image content representation plays a critical role in visual recognition. In this paper, we present a joint dictionary learning (JDL) algorithm which exploits the inter-category visual correlations to learn more discriminative dictionaries. Given a group of visually correlated categories, JDL simultaneously learns one common dictionary and multiple category-specific dictionaries to explicitly separate the shared visual atoms from the category-specific ones. The problem of JDL is formulated as a joint optimization with a discrimination promotion term according to the Fisher discrimination criterion. A visual tree method is developed to cluster a large number of categories into a set of disjoint groups, so that each of them contains a reasonable number of visually correlated categories. The process of image category clustering helps JDL to learn better dictionaries for classification by ensuring that the categories in the same group are of strong visual correlations. Also, it makes JDL to be computationally affordable in large-scale applications. Three classification schemes are adopted to make full use of the dictionaries learned by JDL for visual content representation in the task of image categorization. The effectiveness of the proposed algorithms has been evaluated using two image databases containing 17 and 1,000 categories, respectively.
机译：学习用于图像内容表示的判别词典在视觉识别中起着至关重要的作用。在本文中，我们提出了一种联合字典学习（JDL）算法，该算法利用类别间的视觉相关性来学习更多判别词典。给定一组视觉上相关的类别，JDL同时学习一个通用词典和多个类别特定的字典，以将共享的可视原子与类别特定的可视原子明确分开。根据Fisher判别标准，将JDL问题表述为带有判别促进项的联合优化。开发了一种视觉树方法，以将大量类别聚集成一组不相交的组，以便每个类别都包含合理数量的视觉相关类别。图像类别聚类的过程通过确保同一组中的类别具有很强的视觉相关性，可以帮助JDL学习更好的分类字典。而且，它使JDL在大规模应用程序中在计算上负担得起。在图像分类任务中，采用了三种分类方案来充分利用JDL所学习的词典中用于视觉内容表示的词典。使用分别包含17和1,000个类别的两个图像数据库评估了所提出算法的有效性。

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