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核密度主题模型:视觉主题没有视觉单词

Kernel Density Topic Models: Visual Topics Without Visual Words
课程网址: http://videolectures.net/nipsworkshops2012_rematas_visual/  
主讲教师: Konstantinos Rematas
开课单位: 鲁汶大学
开课时间: 2013-01-16
课程语种: 英语
中文简介:
计算机视觉社区通过将特征空间离散化为视觉词,将最初在文档处理领域开发的技术转移到视觉领域,从而大大受益。本文重新分析了视觉特征连续空间人为离散化的必要性,提出了基于核密度估计的流行主题模型的替代公式。结果表明,该模型的优点在于减少了复杂度,提高了对象发现任务的性能。
课程简介: The computer vision community has greatly benefited from transferring techniques originally developed in the document processing domain to the visual domain by means of discretizing the features space into visual words. This paper reinvestigates the necessity of this artificially discretization of the continuous space of visual features and consequently proposes an alternative formulation of the popular topic models that is based on kernel density estimates. Results indicate the benefits of our model in terms of decreased perplexity as well as improved performance on object discovery tasks.
关 键 词: 计算机视觉领域; 离散; 核密度估计
课程来源: 视频讲座网
最后编审: 2020-06-02:张荧(课程编辑志愿者)
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