Content-based image retrieval system using neural network

Hanen Karamti, Mohamed Tmar, Faiez Gargouri

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

9 Citations (Scopus)

Abstract

Visual information retrieval has become a major research area due to increasing rate at which images are generated in many application. This paper addresses an important problems related to the content-based images retrieval. It concerns the vector representation of images and its proper use in image retrieval. Indeed, we propose a new model of content-based image retrieval allowing to integrate theories of neural network on a vector space model, where each low level query can be transformed into a score vector. Preliminary results obtained show that our proposed model is effective in a comparative study on two dataset Corel and Caltech-UCSD.

Original languageEnglish
Title of host publication2014 IEEE/ACS 11th International Conference on Computer Systems and Applications, AICCSA 2014
PublisherIEEE Computer Society
Pages723-728
Number of pages6
Volume2014
ISBN (Electronic)9781479971008
DOIs
Publication statusPublished - 1 Jan 2014
Event2014 11th IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 2014 - Doha, Qatar
Duration: 10 Nov 201413 Nov 2014

Other

Other2014 11th IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 2014
CountryQatar
CityDoha
Period10/11/1413/11/14

Fingerprint

Image retrieval
Neural networks
Vector spaces
Information retrieval

Cite this

Karamti, H., Tmar, M., & Gargouri, F. (2014). Content-based image retrieval system using neural network. In 2014 IEEE/ACS 11th International Conference on Computer Systems and Applications, AICCSA 2014 (Vol. 2014, pp. 723-728). [7073271] IEEE Computer Society. https://doi.org/10.1109/AICCSA.2014.7073271
Karamti, Hanen ; Tmar, Mohamed ; Gargouri, Faiez. / Content-based image retrieval system using neural network. 2014 IEEE/ACS 11th International Conference on Computer Systems and Applications, AICCSA 2014. Vol. 2014 IEEE Computer Society, 2014. pp. 723-728
@inproceedings{d929d3b9a4764eaca94cafbf640fd1d3,
title = "Content-based image retrieval system using neural network",
abstract = "Visual information retrieval has become a major research area due to increasing rate at which images are generated in many application. This paper addresses an important problems related to the content-based images retrieval. It concerns the vector representation of images and its proper use in image retrieval. Indeed, we propose a new model of content-based image retrieval allowing to integrate theories of neural network on a vector space model, where each low level query can be transformed into a score vector. Preliminary results obtained show that our proposed model is effective in a comparative study on two dataset Corel and Caltech-UCSD.",
author = "Hanen Karamti and Mohamed Tmar and Faiez Gargouri",
year = "2014",
month = "1",
day = "1",
doi = "10.1109/AICCSA.2014.7073271",
language = "English",
volume = "2014",
pages = "723--728",
booktitle = "2014 IEEE/ACS 11th International Conference on Computer Systems and Applications, AICCSA 2014",
publisher = "IEEE Computer Society",
address = "United States",

}

Karamti, H, Tmar, M & Gargouri, F 2014, Content-based image retrieval system using neural network. in 2014 IEEE/ACS 11th International Conference on Computer Systems and Applications, AICCSA 2014. vol. 2014, 7073271, IEEE Computer Society, pp. 723-728, 2014 11th IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 2014, Doha, Qatar, 10/11/14. https://doi.org/10.1109/AICCSA.2014.7073271

Content-based image retrieval system using neural network. / Karamti, Hanen; Tmar, Mohamed; Gargouri, Faiez.

2014 IEEE/ACS 11th International Conference on Computer Systems and Applications, AICCSA 2014. Vol. 2014 IEEE Computer Society, 2014. p. 723-728 7073271.

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

TY - GEN

T1 - Content-based image retrieval system using neural network

AU - Karamti, Hanen

AU - Tmar, Mohamed

AU - Gargouri, Faiez

PY - 2014/1/1

Y1 - 2014/1/1

N2 - Visual information retrieval has become a major research area due to increasing rate at which images are generated in many application. This paper addresses an important problems related to the content-based images retrieval. It concerns the vector representation of images and its proper use in image retrieval. Indeed, we propose a new model of content-based image retrieval allowing to integrate theories of neural network on a vector space model, where each low level query can be transformed into a score vector. Preliminary results obtained show that our proposed model is effective in a comparative study on two dataset Corel and Caltech-UCSD.

AB - Visual information retrieval has become a major research area due to increasing rate at which images are generated in many application. This paper addresses an important problems related to the content-based images retrieval. It concerns the vector representation of images and its proper use in image retrieval. Indeed, we propose a new model of content-based image retrieval allowing to integrate theories of neural network on a vector space model, where each low level query can be transformed into a score vector. Preliminary results obtained show that our proposed model is effective in a comparative study on two dataset Corel and Caltech-UCSD.

UR - http://www.scopus.com/inward/record.url?scp=84988273577&partnerID=8YFLogxK

U2 - 10.1109/AICCSA.2014.7073271

DO - 10.1109/AICCSA.2014.7073271

M3 - Conference contribution

VL - 2014

SP - 723

EP - 728

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

PB - IEEE Computer Society

ER -

Karamti H, Tmar M, Gargouri F. Content-based image retrieval system using neural network. In 2014 IEEE/ACS 11th International Conference on Computer Systems and Applications, AICCSA 2014. Vol. 2014. IEEE Computer Society. 2014. p. 723-728. 7073271 https://doi.org/10.1109/AICCSA.2014.7073271