Scale and topology preserving SIFT feature hashing

Chen Kang; Li Zhu; Xueming Qian

doi:10.1007/978-3-319-48896-7_19

Scale and topology preserving SIFT feature hashing

Chen Kang
, Li Zhu
, Xueming Qian

Faculty of Electronic and Information Engineering

Xi'an Jiaotong University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Abstract. In recent years, content based image retrieval has been concerned because of practical needs on Internet services, especially methods that can improve retrieving speed and precision. Thus, we propose a hashing scheme called Geometry and Topology Preserving Hashing for content based image retrieval. A training process of hashing function involves both of geometric information and topology information is introduced. Compared with state-of-the-art methods, our method gives better precision in experiment on the Oxford Building dataset.

Original language	English
Title of host publication	Advances in Multimedia Information Processing – 17th Pacific-Rim Conference on Multimedia, PCM 2016, Proceedings
Editors	Enqing Chen, Yun Tie, Yihong Gong
Publisher	Springer Verlag
Pages	190-199
Number of pages	10
ISBN (Print)	9783319488950
DOIs	https://doi.org/10.1007/978-3-319-48896-7_19
State	Published - 2016
Event	17th Pacific-Rim Conference on Multimedia, PCM 2016 - Xi’an, China Duration: 15 Sep 2016 → 16 Sep 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9917 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	17th Pacific-Rim Conference on Multimedia, PCM 2016
Country/Territory	China
City	Xi’an
Period	15/09/16 → 16/09/16

Keywords

CBIR
GTPH
Geometric information
Hashing
SIFT

Access to Document

10.1007/978-3-319-48896-7_19

Cite this

Kang, C., Zhu, L., & Qian, X. (2016). Scale and topology preserving SIFT feature hashing. In E. Chen, Y. Tie, & Y. Gong (Eds.), Advances in Multimedia Information Processing – 17th Pacific-Rim Conference on Multimedia, PCM 2016, Proceedings (pp. 190-199). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9917 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-48896-7_19

Kang, Chen ; Zhu, Li ; Qian, Xueming. / Scale and topology preserving SIFT feature hashing. Advances in Multimedia Information Processing – 17th Pacific-Rim Conference on Multimedia, PCM 2016, Proceedings. editor / Enqing Chen ; Yun Tie ; Yihong Gong. Springer Verlag, 2016. pp. 190-199 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Abstract. In recent years, content based image retrieval has been concerned because of practical needs on Internet services, especially methods that can improve retrieving speed and precision. Thus, we propose a hashing scheme called Geometry and Topology Preserving Hashing for content based image retrieval. A training process of hashing function involves both of geometric information and topology information is introduced. Compared with state-of-the-art methods, our method gives better precision in experiment on the Oxford Building dataset.",

keywords = "CBIR, GTPH, Geometric information, Hashing, SIFT",

author = "Chen Kang and Li Zhu and Xueming Qian",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; 17th Pacific-Rim Conference on Multimedia, PCM 2016 ; Conference date: 15-09-2016 Through 16-09-2016",

year = "2016",

doi = "10.1007/978-3-319-48896-7\_19",

language = "英语",

isbn = "9783319488950",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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editor = "Enqing Chen and Yun Tie and Yihong Gong",

booktitle = "Advances in Multimedia Information Processing – 17th Pacific-Rim Conference on Multimedia, PCM 2016, Proceedings",

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Kang, C, Zhu, L & Qian, X 2016, Scale and topology preserving SIFT feature hashing. in E Chen, Y Tie & Y Gong (eds), Advances in Multimedia Information Processing – 17th Pacific-Rim Conference on Multimedia, PCM 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9917 LNCS, Springer Verlag, pp. 190-199, 17th Pacific-Rim Conference on Multimedia, PCM 2016, Xi’an, China, 15/09/16. https://doi.org/10.1007/978-3-319-48896-7_19

Scale and topology preserving SIFT feature hashing. / Kang, Chen; Zhu, Li; Qian, Xueming.
Advances in Multimedia Information Processing – 17th Pacific-Rim Conference on Multimedia, PCM 2016, Proceedings. ed. / Enqing Chen; Yun Tie; Yihong Gong. Springer Verlag, 2016. p. 190-199 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9917 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Scale and topology preserving SIFT feature hashing

AU - Kang, Chen

AU - Zhu, Li

AU - Qian, Xueming

N1 - Publisher Copyright: © Springer International Publishing AG 2016.

PY - 2016

Y1 - 2016

N2 - Abstract. In recent years, content based image retrieval has been concerned because of practical needs on Internet services, especially methods that can improve retrieving speed and precision. Thus, we propose a hashing scheme called Geometry and Topology Preserving Hashing for content based image retrieval. A training process of hashing function involves both of geometric information and topology information is introduced. Compared with state-of-the-art methods, our method gives better precision in experiment on the Oxford Building dataset.

AB - Abstract. In recent years, content based image retrieval has been concerned because of practical needs on Internet services, especially methods that can improve retrieving speed and precision. Thus, we propose a hashing scheme called Geometry and Topology Preserving Hashing for content based image retrieval. A training process of hashing function involves both of geometric information and topology information is introduced. Compared with state-of-the-art methods, our method gives better precision in experiment on the Oxford Building dataset.

KW - CBIR

KW - GTPH

KW - Geometric information

KW - Hashing

KW - SIFT

UR - https://www.scopus.com/pages/publications/85006954459

U2 - 10.1007/978-3-319-48896-7_19

DO - 10.1007/978-3-319-48896-7_19

M3 - 会议稿件

AN - SCOPUS:85006954459

SN - 9783319488950

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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EP - 199

BT - Advances in Multimedia Information Processing – 17th Pacific-Rim Conference on Multimedia, PCM 2016, Proceedings

A2 - Chen, Enqing

A2 - Tie, Yun

A2 - Gong, Yihong

PB - Springer Verlag

T2 - 17th Pacific-Rim Conference on Multimedia, PCM 2016

Y2 - 15 September 2016 through 16 September 2016

ER -

Kang C, Zhu L, Qian X. Scale and topology preserving SIFT feature hashing. In Chen E, Tie Y, Gong Y, editors, Advances in Multimedia Information Processing – 17th Pacific-Rim Conference on Multimedia, PCM 2016, Proceedings. Springer Verlag. 2016. p. 190-199. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-48896-7_19

Scale and topology preserving SIFT feature hashing

Abstract

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Keywords

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