Matching with quantum genetic algorithm and shape contexts

Khalil M. Mezghiche, Kamal E. Melkemi, Sebti Foufou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we propose to combine the shape context (SC) descriptor with quantum genetic algorithms (QGA) to define a new shape matching and retrieval method. The SC matching method is based on finding the best correspondence between two point sets. The proposed method uses the QGA to find the best configuration of sample points in order to achieve the best possible matching between the two shapes. This combination of SC and QGA leads to a better retrieval results based on our tests. The SC is a very powerful discriminative descriptor which is translation and scale invariant, but weak against rotation and flipping. In our proposed quantum shape context algorithm (QSC), we use the QGA to estimate the best orientation of the target shape to ensure the best matching for rotated and flipped shapes. The experimental results showed that our proposed QSC matching method is much powerful than the classic SC method for the retrieval of shapes with orientation changes.

Original languageEnglish (US)
Title of host publication2014 IEEE/ACS 11th International Conference on Computer Systems and Applications, AICCSA 2014
PublisherIEEE Computer Society
Pages536-542
Number of pages7
Volume2014
ISBN (Electronic)9781479971008
DOIs
StatePublished - Jan 1 2014
Event2014 11th IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 2014 - Doha, Qatar
Duration: Nov 10 2014Nov 13 2014

Other

Other2014 11th IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 2014
CountryQatar
CityDoha
Period11/10/1411/13/14

Fingerprint

Genetic algorithms

Keywords

  • quantum genetic algorithm
  • shape context
  • shape matching
  • shape retrieval

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Signal Processing
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Cite this

Mezghiche, K. M., Melkemi, K. E., & Foufou, S. (2014). Matching with quantum genetic algorithm and shape contexts. In 2014 IEEE/ACS 11th International Conference on Computer Systems and Applications, AICCSA 2014 (Vol. 2014, pp. 536-542). [7073245] IEEE Computer Society. https://doi.org/10.1109/AICCSA.2014.7073245

Matching with quantum genetic algorithm and shape contexts. / Mezghiche, Khalil M.; Melkemi, Kamal E.; Foufou, Sebti.

2014 IEEE/ACS 11th International Conference on Computer Systems and Applications, AICCSA 2014. Vol. 2014 IEEE Computer Society, 2014. p. 536-542 7073245.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Mezghiche, KM, Melkemi, KE & Foufou, S 2014, Matching with quantum genetic algorithm and shape contexts. in 2014 IEEE/ACS 11th International Conference on Computer Systems and Applications, AICCSA 2014. vol. 2014, 7073245, IEEE Computer Society, pp. 536-542, 2014 11th IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 2014, Doha, Qatar, 11/10/14. https://doi.org/10.1109/AICCSA.2014.7073245
Mezghiche KM, Melkemi KE, Foufou S. Matching with quantum genetic algorithm and shape contexts. In 2014 IEEE/ACS 11th International Conference on Computer Systems and Applications, AICCSA 2014. Vol. 2014. IEEE Computer Society. 2014. p. 536-542. 7073245 https://doi.org/10.1109/AICCSA.2014.7073245
Mezghiche, Khalil M. ; Melkemi, Kamal E. ; Foufou, Sebti. / Matching with quantum genetic algorithm and shape contexts. 2014 IEEE/ACS 11th International Conference on Computer Systems and Applications, AICCSA 2014. Vol. 2014 IEEE Computer Society, 2014. pp. 536-542
@inproceedings{602664152df5425d8efaac1d335ed1b3,
title = "Matching with quantum genetic algorithm and shape contexts",
abstract = "In this paper, we propose to combine the shape context (SC) descriptor with quantum genetic algorithms (QGA) to define a new shape matching and retrieval method. The SC matching method is based on finding the best correspondence between two point sets. The proposed method uses the QGA to find the best configuration of sample points in order to achieve the best possible matching between the two shapes. This combination of SC and QGA leads to a better retrieval results based on our tests. The SC is a very powerful discriminative descriptor which is translation and scale invariant, but weak against rotation and flipping. In our proposed quantum shape context algorithm (QSC), we use the QGA to estimate the best orientation of the target shape to ensure the best matching for rotated and flipped shapes. The experimental results showed that our proposed QSC matching method is much powerful than the classic SC method for the retrieval of shapes with orientation changes.",
keywords = "quantum genetic algorithm, shape context, shape matching, shape retrieval",
author = "Mezghiche, {Khalil M.} and Melkemi, {Kamal E.} and Sebti Foufou",
year = "2014",
month = "1",
day = "1",
doi = "10.1109/AICCSA.2014.7073245",
language = "English (US)",
volume = "2014",
pages = "536--542",
booktitle = "2014 IEEE/ACS 11th International Conference on Computer Systems and Applications, AICCSA 2014",
publisher = "IEEE Computer Society",

}

TY - GEN

T1 - Matching with quantum genetic algorithm and shape contexts

AU - Mezghiche, Khalil M.

AU - Melkemi, Kamal E.

AU - Foufou, Sebti

PY - 2014/1/1

Y1 - 2014/1/1

N2 - In this paper, we propose to combine the shape context (SC) descriptor with quantum genetic algorithms (QGA) to define a new shape matching and retrieval method. The SC matching method is based on finding the best correspondence between two point sets. The proposed method uses the QGA to find the best configuration of sample points in order to achieve the best possible matching between the two shapes. This combination of SC and QGA leads to a better retrieval results based on our tests. The SC is a very powerful discriminative descriptor which is translation and scale invariant, but weak against rotation and flipping. In our proposed quantum shape context algorithm (QSC), we use the QGA to estimate the best orientation of the target shape to ensure the best matching for rotated and flipped shapes. The experimental results showed that our proposed QSC matching method is much powerful than the classic SC method for the retrieval of shapes with orientation changes.

AB - In this paper, we propose to combine the shape context (SC) descriptor with quantum genetic algorithms (QGA) to define a new shape matching and retrieval method. The SC matching method is based on finding the best correspondence between two point sets. The proposed method uses the QGA to find the best configuration of sample points in order to achieve the best possible matching between the two shapes. This combination of SC and QGA leads to a better retrieval results based on our tests. The SC is a very powerful discriminative descriptor which is translation and scale invariant, but weak against rotation and flipping. In our proposed quantum shape context algorithm (QSC), we use the QGA to estimate the best orientation of the target shape to ensure the best matching for rotated and flipped shapes. The experimental results showed that our proposed QSC matching method is much powerful than the classic SC method for the retrieval of shapes with orientation changes.

KW - quantum genetic algorithm

KW - shape context

KW - shape matching

KW - shape retrieval

UR - http://www.scopus.com/inward/record.url?scp=84940857689&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84940857689&partnerID=8YFLogxK

U2 - 10.1109/AICCSA.2014.7073245

DO - 10.1109/AICCSA.2014.7073245

M3 - Conference contribution

AN - SCOPUS:84940857689

VL - 2014

SP - 536

EP - 542

BT - 2014 IEEE/ACS 11th International Conference on Computer Systems and Applications, AICCSA 2014

PB - IEEE Computer Society

ER -