Online Real-time Image Retrieval Based on Large-scale Vocabulary Tree

被引：0
|
作者：
Han Shiwei [1 ]
Li Jing [1 ]
Yang Tao [2 ]
Lu Zhaoyang [1 ]
Zhang Fangbing [1 ]
Wei Lisong [1 ]
机构：
[1] Xidian Univ, Sch Telecommun Engn, Xian 710071, Peoples R China
[2] Northwestern Polytech Univ, Sch Comp Sci & Engn, Xian 710071, Peoples R China
来源：
PROCEEDINGS OF 2016 IEEE 13TH INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING (ICSP 2016) | 2016年
基金：
中国国家自然科学基金;
关键词：
vocabulary tree; image retrieval; large-scale dataset; bag-of-words;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
This paper presents a novel method for online real-time content-based image training and retrieval. The method relies on bags-of-words with SIFT features, and data can be extracted from a generated large scale vocabulary tree to describe all kind of images. The large-scale vocabulary tree can be seen as a code book that new images can be described. We use a large-scale vocabulary tree to generate vectors for new images, and compare the similarity of vectors between the database and query is a feasible way to achieve retrieval. Experimental results prove that the proposed method can achieve a good performance.
引用
收藏
页码：953 / 956
页数：4
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