ELIS: An efficient leaf image retrieval system

Yunyoung Nam; Eenjun Hwang; Kwangjun Byeon

doi:10.1007/11552499_65

ELIS: An efficient leaf image retrieval system

Yunyoung Nam, Eenjun Hwang, Kwangjun Byeon

School of Electrical Engineering

Research output: Contribution to journal › Conference article › peer-review

6 Citations (Scopus)

Abstract

In this paper, we present an effective and robust shape-based leaf image retrieval system that supports two novel features: improved MPP algorithm and revised dynamic matching method. The improved MPP algorithm reduces the number of points for the shape representation considerably. Moreover, the new dynamic matching method, which is a revised Nearest Neighbor search, reduces the matching time. We implemented a prototype system based on these features and performed several experiments to show its effectiveness. We compare its performance with other known methods and report some of the results.

Original language	English
Pages (from-to)	589-597
Number of pages	9
Journal	Lecture Notes in Computer Science
Volume	3687
Issue number	PART II
DOIs	https://doi.org/10.1007/11552499_65
Publication status	Published - 2005
Event	Third International Conference on Advances in Patten Recognition, ICAPR 2005 - Bath, United Kingdom Duration: 2005 Aug 22 → 2005 Aug 25

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

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10.1007/11552499_65

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title = "ELIS: An efficient leaf image retrieval system",

abstract = "In this paper, we present an effective and robust shape-based leaf image retrieval system that supports two novel features: improved MPP algorithm and revised dynamic matching method. The improved MPP algorithm reduces the number of points for the shape representation considerably. Moreover, the new dynamic matching method, which is a revised Nearest Neighbor search, reduces the matching time. We implemented a prototype system based on these features and performed several experiments to show its effectiveness. We compare its performance with other known methods and report some of the results.",

author = "Yunyoung Nam and Eenjun Hwang and Kwangjun Byeon",

note = "Funding Information: This research was supported by University IT Research Center Project and a grant (no. BDM0100211) to JRL from the Strategic National R&D Program through the Genetic Resources and Information Network Center funded by the Korean Ministry of Science and Technology.; Third International Conference on Advances in Patten Recognition, ICAPR 2005 ; Conference date: 22-08-2005 Through 25-08-2005",

year = "2005",

doi = "10.1007/11552499_65",

language = "English",

volume = "3687",

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

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number = "PART II",

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T1 - ELIS

T2 - Third International Conference on Advances in Patten Recognition, ICAPR 2005

AU - Nam, Yunyoung

AU - Hwang, Eenjun

AU - Byeon, Kwangjun

N1 - Funding Information: This research was supported by University IT Research Center Project and a grant (no. BDM0100211) to JRL from the Strategic National R&D Program through the Genetic Resources and Information Network Center funded by the Korean Ministry of Science and Technology.

PY - 2005

Y1 - 2005

N2 - In this paper, we present an effective and robust shape-based leaf image retrieval system that supports two novel features: improved MPP algorithm and revised dynamic matching method. The improved MPP algorithm reduces the number of points for the shape representation considerably. Moreover, the new dynamic matching method, which is a revised Nearest Neighbor search, reduces the matching time. We implemented a prototype system based on these features and performed several experiments to show its effectiveness. We compare its performance with other known methods and report some of the results.

AB - In this paper, we present an effective and robust shape-based leaf image retrieval system that supports two novel features: improved MPP algorithm and revised dynamic matching method. The improved MPP algorithm reduces the number of points for the shape representation considerably. Moreover, the new dynamic matching method, which is a revised Nearest Neighbor search, reduces the matching time. We implemented a prototype system based on these features and performed several experiments to show its effectiveness. We compare its performance with other known methods and report some of the results.

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

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

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