Object instance recognition using best increasing subsequence

Kristopher David Harjono, Gede Putra Kusuma Negara
{"title":"Object instance recognition using best increasing subsequence","authors":"Kristopher David Harjono, Gede Putra Kusuma Negara","doi":"10.1109/KICSS.2016.7951432","DOIUrl":null,"url":null,"abstract":"Object instance recognition enables the realization of many potential applications, such as information retrieval, scene understanding and human computer interaction. However, it is still a challenging problem in computer vision. The appearance of an object is affected by variations in illumination, viewpoint and occlusion. In this contribution, we propose an object instance recognition method based on Best Increasing Subsequence. It estimates a set of geometrically consistent feature pairs and at the same time, maximizes the total similarity score between test and train images. Our experimental results show that the proposed method outperforms the existing geometric verification methods, RANSAC Homography and Weighted LIS.","PeriodicalId":170692,"journal":{"name":"International Conference on Knowledge, Information, and Creativity Support Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Knowledge, Information, and Creativity Support Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/KICSS.2016.7951432","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

Object instance recognition enables the realization of many potential applications, such as information retrieval, scene understanding and human computer interaction. However, it is still a challenging problem in computer vision. The appearance of an object is affected by variations in illumination, viewpoint and occlusion. In this contribution, we propose an object instance recognition method based on Best Increasing Subsequence. It estimates a set of geometrically consistent feature pairs and at the same time, maximizes the total similarity score between test and train images. Our experimental results show that the proposed method outperforms the existing geometric verification methods, RANSAC Homography and Weighted LIS.
使用最佳递增子序列的对象实例识别
对象实例识别可以实现许多潜在的应用,如信息检索、场景理解和人机交互。然而,它仍然是计算机视觉中的一个具有挑战性的问题。物体的外观受到光照、视点和遮挡变化的影响。在本文中,我们提出了一种基于最佳递增子序列的目标实例识别方法。它估计一组几何上一致的特征对,同时最大化测试图像和训练图像之间的总相似度。实验结果表明,该方法优于现有的几何验证方法、RANSAC单应性和加权LIS。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信