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

页码：953 / 956

页数：4

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