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Sparse Online Learning of Image Similarity

文献类型：期刊论文

作者Gao, Xingyu2,4; Hoi, Steven C. H.3; Zhang, Yongdong4; Zhou, Jianshe5; Wan, Ji4; Chen, Zhenyu1; Li, Jintao4; Zhu, Jianke6
刊名ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
出版日期2017-09-01
卷号8期号:5页码:22
关键词Online learning metric learning similarity learning distance metric bag-of-words representation image retrieval
ISSN号2157-6904
DOI10.1145/3065950
英文摘要Learning image similarity plays a critical role in real-world multimedia information retrieval applications, especially in Content-Based Image Retrieval (CBIR) tasks, in which an accurate retrieval of visually similar objects largely relies on an effective image similarity function. Crafting a good similarity function is very challenging because visual contents of images are often represented as feature vectors in high-dimensional spaces, for example, via bag-of-words (BoW) representations, and traditional rigid similarity functions, for example, cosine similarity, are often suboptimal for CBIR tasks. In this article, we address this fundamental problem, that is, learning to optimize image similarity with sparse and high-dimensional representations from large-scale training data, and propose a novel scheme of Sparse Online Learning of Image Similarity (SOLIS). In contrast to many existing image-similarity learning algorithms that are designed to work with low-dimensional data, SOLIS is able to learn image similarity from large-scale image data in sparse and high-dimensional spaces. Our encouraging results showed that the proposed new technique achieves highly competitive accuracy as compared to the state-of-the-art approaches but enjoys significant advantages in computational efficiency, model sparsity, and retrieval scalability, making it more practical for real-world multimedia retrieval applications.
资助项目National Key Research and Development Program of China[2016YFB0800403] ; National Nature Science Foundation of China[61525206] ; National Nature Science Foundation of China[61572472] ; National Nature Science Foundation of China[61428207] ; Singapore Ministry of Education (MOE) Academic Research Fund (AcRF) Tier 1 Grant ; Beijing Natural Science Foundation[4152050] ; Beijing Advanced Innovation Center for Imaging Technology[BAICIT-2016009]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000414318500003
出版者ASSOC COMPUTING MACHINERY
源URL[http://119.78.100.204/handle/2XEOYT63/6467]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Hoi, Steven C. H.; Zhang, Yongdong
作者单位1.Chinese Acad Sci, China Elect Power Res Inst, Key Lab Intelligent Informat Proc, Inst Comp Technol, Beijing 100192, Peoples R China
2.Chinese Acad Sci, Inst Software, Lab Parallel Software & Computat Sci, Beijing 100190, Peoples R China
3.Singapore Management Univ, Sch Informat Syst, Singapore 188065, Singapore
4.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
5.Capital Normal Univ, Beijing Adv Innovat Ctr Imaging Technol, Beijing 100048, Peoples R China
6.Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou 310058, Zhejiang, Peoples R China
推荐引用方式
GB/T 7714		 Gao, Xingyu,Hoi, Steven C. H.,Zhang, Yongdong,et al. Sparse Online Learning of Image Similarity[J]. ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY,2017,8(5):22.
APA Gao, Xingyu.,Hoi, Steven C. H..,Zhang, Yongdong.,Zhou, Jianshe.,Wan, Ji.,...&Zhu, Jianke.(2017).Sparse Online Learning of Image Similarity.ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY,8(5),22.
MLA Gao, Xingyu,et al."Sparse Online Learning of Image Similarity".ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY 8.5(2017):22.

入库方式： OAI收割

来源：计算技术研究所

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