Object Recognition using Bag of words with Kernels for Big Data

被引：0

作者：

Wu, Cheyu ^{[1
]}

Chiu, Ching-Te ^{[2
]}

Hsu, Yar-Sun ^{[1
]}

机构：

[1] Natl Tsing Hua Univ, Dept Elect Engn, Hsinchu, Taiwan

[2] Natl Tsing Hua Univ, Dept Comp Sci, Hsinchu, Taiwan

来源：

2014 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS - TAIWAN (ICCE-TW) | 2014年

关键词：

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：

Scale Invariant Feature Transform (SIFT) descriptor can represent the object in detail, and is robust to variations due to image scaling and illumination changes. The challenge of using such descriptor to perform image retrieval in a large scale database is the high computational complexity. In this paper, we present the bag of words model combined with SIFT to reduce the computation cost. The average precision we get is about 30%.

引用

页数：2

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