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
N1 - Publisher Copyright:
© 2003 IEEE.
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
T3 - Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA
SP - 994
EP - 997
BT - Proceedings - 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA 2003
Y2 - 16 July 2003 through 20 July 2003
ER -