.png){kind=icon}
返回
Content-based image retrieval using high-dimensional information geometry
.png){kind=icon}
PRE
AI
DOI：10.1007/s11432-014-5086-8.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, a new content-based image retrieval approach is proposed based on high- dimensional information theory. The proposed approach overcomes the disadvantages of the current content-based image retrieval algorithms that suffer from the semantic gap. First, we present a new multidimensional information space's vector angle cosine algorithm of high-dimensional geometry, then, we provide a detailed description of our images retrieval method including proposal of an overlapping image block method and definition of a similarity degree between images on the non-dimensional information subspaces. Finally, experimental results show the higher retrieval efficiency of the proposed algorithm.
Keyword:
image retrieval
angel cosine
high-dimensional information
feature extraction
information subspace
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.6
论文数:
4.9K
被引数:
8.9K
机构
引用论文
Group sparse representation for image categorization and semantic video retrieval用于图像分类和语义视频检索的组稀疏表示
.png){kind=icon}
PREAI
分享收藏分享收藏
没有更多内容
没有更多内容