Scene image analysis by using the sandglass-type neural network with a factor analysis

Seiji Ito, Yasue Mitsukura, Minoru Fukumi, Norio Akamatsu, Sigeru Omatu

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

2 Citations (Scopus)

Abstract

It is difficult to obtain images only we want on the web. Because, enormous data exist in the web. A present detection system of images are keyword detection which is added the name of keyword for images. Therefore, it is very important and difficult to add the keyword for images. In this paper, keywords in the image are anahzed by using the factor analysis and the sandglass-type neural network (SNN) for image searching. As images preprocessing, objective images are segmented by the maximin-distance algorithm. Small regions are integrated into a near region. Thus, objective images are segmented into some region. After mis images preprocessing, keywords in images are analyzed by using factor analysis and a sandglass-type neural network (SNN) for image searching in this paper. Images data are compressed to a 2-dimensional space by using these two methods. This 2-dimensional data space is presented by a graph. Thus, keywords are analyzed in detail.

Original languageEnglish
Title of host publicationProceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages994-997
Number of pages4
Volume2
ISBN (Print)0780378660
DOIs
Publication statusPublished - 2003
Externally publishedYes
Event2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA 2003 - Kobe, Japan
Duration: 2003 Jul 162003 Jul 20

Other

Other2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA 2003
CountryJapan
CityKobe
Period03/7/1603/7/20

Fingerprint

Scene Analysis
Factor analysis
Factor Analysis
Image Analysis
Image analysis
Neural Networks
Neural networks
Preprocessing
Maximin

Keywords

  • Factor analysis
  • Integrated small region
  • Maximin-distance algorithm
  • Median filtering
  • SNN

ASJC Scopus subject areas

  • Computational Mathematics

Cite this

Ito, S., Mitsukura, Y., Fukumi, M., Akamatsu, N., & Omatu, S. (2003). Scene image analysis by using the sandglass-type neural network with a factor analysis. In Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA (Vol. 2, pp. 994-997). [1222315] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CIRA.2003.1222315

Scene image analysis by using the sandglass-type neural network with a factor analysis. / Ito, Seiji; Mitsukura, Yasue; Fukumi, Minoru; Akamatsu, Norio; Omatu, Sigeru.

Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA. Vol. 2 Institute of Electrical and Electronics Engineers Inc., 2003. p. 994-997 1222315.

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

Ito, S, Mitsukura, Y, Fukumi, M, Akamatsu, N & Omatu, S 2003, Scene image analysis by using the sandglass-type neural network with a factor analysis. in Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA. vol. 2, 1222315, Institute of Electrical and Electronics Engineers Inc., pp. 994-997, 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA 2003, Kobe, Japan, 03/7/16. https://doi.org/10.1109/CIRA.2003.1222315
Ito S, Mitsukura Y, Fukumi M, Akamatsu N, Omatu S. Scene image analysis by using the sandglass-type neural network with a factor analysis. In Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA. Vol. 2. Institute of Electrical and Electronics Engineers Inc. 2003. p. 994-997. 1222315 https://doi.org/10.1109/CIRA.2003.1222315
Ito, Seiji ; Mitsukura, Yasue ; Fukumi, Minoru ; Akamatsu, Norio ; Omatu, Sigeru. / Scene image analysis by using the sandglass-type neural network with a factor analysis. Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA. Vol. 2 Institute of Electrical and Electronics Engineers Inc., 2003. pp. 994-997
@inproceedings{5bfdebdce40a47e5b536a87b60ed74fd,
title = "Scene image analysis by using the sandglass-type neural network with a factor analysis",
abstract = "It is difficult to obtain images only we want on the web. Because, enormous data exist in the web. A present detection system of images are keyword detection which is added the name of keyword for images. Therefore, it is very important and difficult to add the keyword for images. In this paper, keywords in the image are anahzed by using the factor analysis and the sandglass-type neural network (SNN) for image searching. As images preprocessing, objective images are segmented by the maximin-distance algorithm. Small regions are integrated into a near region. Thus, objective images are segmented into some region. After mis images preprocessing, keywords in images are analyzed by using factor analysis and a sandglass-type neural network (SNN) for image searching in this paper. Images data are compressed to a 2-dimensional space by using these two methods. This 2-dimensional data space is presented by a graph. Thus, keywords are analyzed in detail.",
keywords = "Factor analysis, Integrated small region, Maximin-distance algorithm, Median filtering, SNN",
author = "Seiji Ito and Yasue Mitsukura and Minoru Fukumi and Norio Akamatsu and Sigeru Omatu",
year = "2003",
doi = "10.1109/CIRA.2003.1222315",
language = "English",
isbn = "0780378660",
volume = "2",
pages = "994--997",
booktitle = "Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - GEN

T1 - Scene image analysis by using the sandglass-type neural network with a factor analysis

AU - Ito, Seiji

AU - Mitsukura, Yasue

AU - Fukumi, Minoru

AU - Akamatsu, Norio

AU - Omatu, Sigeru

PY - 2003

Y1 - 2003

N2 - It is difficult to obtain images only we want on the web. Because, enormous data exist in the web. A present detection system of images are keyword detection which is added the name of keyword for images. Therefore, it is very important and difficult to add the keyword for images. In this paper, keywords in the image are anahzed by using the factor analysis and the sandglass-type neural network (SNN) for image searching. As images preprocessing, objective images are segmented by the maximin-distance algorithm. Small regions are integrated into a near region. Thus, objective images are segmented into some region. After mis images preprocessing, keywords in images are analyzed by using factor analysis and a sandglass-type neural network (SNN) for image searching in this paper. Images data are compressed to a 2-dimensional space by using these two methods. This 2-dimensional data space is presented by a graph. Thus, keywords are analyzed in detail.

AB - It is difficult to obtain images only we want on the web. Because, enormous data exist in the web. A present detection system of images are keyword detection which is added the name of keyword for images. Therefore, it is very important and difficult to add the keyword for images. In this paper, keywords in the image are anahzed by using the factor analysis and the sandglass-type neural network (SNN) for image searching. As images preprocessing, objective images are segmented by the maximin-distance algorithm. Small regions are integrated into a near region. Thus, objective images are segmented into some region. After mis images preprocessing, keywords in images are analyzed by using factor analysis and a sandglass-type neural network (SNN) for image searching in this paper. Images data are compressed to a 2-dimensional space by using these two methods. This 2-dimensional data space is presented by a graph. Thus, keywords are analyzed in detail.

KW - Factor analysis

KW - Integrated small region

KW - Maximin-distance algorithm

KW - Median filtering

KW - SNN

UR - http://www.scopus.com/inward/record.url?scp=48349117886&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=48349117886&partnerID=8YFLogxK

U2 - 10.1109/CIRA.2003.1222315

DO - 10.1109/CIRA.2003.1222315

M3 - Conference contribution

AN - SCOPUS:48349117886

SN - 0780378660

VL - 2

SP - 994

EP - 997

BT - Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA

PB - Institute of Electrical and Electronics Engineers Inc.

ER -