The image recognition system by using the FA and SNN

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

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

1 Citation (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 analized 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 this 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 corresponded to 2-dimensional data by using these two methods. 2-dimensional data are plotted on a graph. Images are recognized by using this graph.

Original languageEnglish
Title of host publicationLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
EditorsV. Palade, R.J. Howlett, L. Jain
Pages578-584
Number of pages7
Volume2773 PART 1
Publication statusPublished - 2003
Externally publishedYes
Event7th International Conference, KES 2003 - Oxford, United Kingdom
Duration: 2003 Sep 32003 Sep 5

Other

Other7th International Conference, KES 2003
CountryUnited Kingdom
CityOxford
Period03/9/303/9/5

Fingerprint

Image recognition
Factor analysis
Neural networks

ASJC Scopus subject areas

  • Hardware and Architecture

Cite this

Ito, S., Mitsukura, Y., Fukumi, M., Akamatsu, N., & Omatu, S. (2003). The image recognition system by using the FA and SNN. In V. Palade, R. J. Howlett, & L. Jain (Eds.), Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 2773 PART 1, pp. 578-584)

The image recognition system by using the FA and SNN. / Ito, Seiji; Mitsukura, Yasue; Fukumi, Minoru; Akamatsu, Norio; Omatu, Sigeru.

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). ed. / V. Palade; R.J. Howlett; L. Jain. Vol. 2773 PART 1 2003. p. 578-584.

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

Ito, S, Mitsukura, Y, Fukumi, M, Akamatsu, N & Omatu, S 2003, The image recognition system by using the FA and SNN. in V Palade, RJ Howlett & L Jain (eds), Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). vol. 2773 PART 1, pp. 578-584, 7th International Conference, KES 2003, Oxford, United Kingdom, 03/9/3.
Ito S, Mitsukura Y, Fukumi M, Akamatsu N, Omatu S. The image recognition system by using the FA and SNN. In Palade V, Howlett RJ, Jain L, editors, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). Vol. 2773 PART 1. 2003. p. 578-584
Ito, Seiji ; Mitsukura, Yasue ; Fukumi, Minoru ; Akamatsu, Norio ; Omatu, Sigeru. / The image recognition system by using the FA and SNN. Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). editor / V. Palade ; R.J. Howlett ; L. Jain. Vol. 2773 PART 1 2003. pp. 578-584
@inproceedings{e5d8cf76429746948f26b9c3acaea05e,
title = "The image recognition system by using the FA and SNN",
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 analized 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 this 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 corresponded to 2-dimensional data by using these two methods. 2-dimensional data are plotted on a graph. Images are recognized by using this graph.",
author = "Seiji Ito and Yasue Mitsukura and Minoru Fukumi and Norio Akamatsu and Sigeru Omatu",
year = "2003",
language = "English",
volume = "2773 PART 1",
pages = "578--584",
editor = "V. Palade and R.J. Howlett and L. Jain",
booktitle = "Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)",

}

TY - GEN

T1 - The image recognition system by using the FA and SNN

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 analized 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 this 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 corresponded to 2-dimensional data by using these two methods. 2-dimensional data are plotted on a graph. Images are recognized by using this graph.

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 analized 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 this 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 corresponded to 2-dimensional data by using these two methods. 2-dimensional data are plotted on a graph. Images are recognized by using this graph.

UR - http://www.scopus.com/inward/record.url?scp=8344285068&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=8344285068&partnerID=8YFLogxK

M3 - Conference contribution

VL - 2773 PART 1

SP - 578

EP - 584

BT - Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

A2 - Palade, V.

A2 - Howlett, R.J.

A2 - Jain, L.

ER -