Image Texture Classification and Retrieval Using Self-Organizing Map

被引:0
|
作者
Thakare, Vishal S. [1 ]
Patil, Nitin N. [1 ]
机构
[1] SESs RC Patel Inst Technol Shirpur, Dept Comp Engn, Shirpur, Maharashtra, India
关键词
Image texture; RGB color; retrieval; Self-organizing map (SOM); Gray level co-occurrence matrix (GLCM);
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Nowadays there has been great interest in field of image texture classification and retrieval. The increasing use of digital images has increased the size of image database which resulted in the need to develop a system that will classify and retrieve the required image of interest efficiently and accurately. This paper presents an effective and accurate method to classify and retrieve image using Self-organizing maps (SOM). The proposed method employs two phases, in the first phase color histogram is used to extract the color features and then the extracted features are given to Self-organizing map for initial classification. In the second phase Gray level co-occurrence matrix (GLCM) is used to extract the texture information from all images in each class from initial classification and then again given to Self-organizing map for final classification. The experimental results show the efficiency of the proposed method.
引用
收藏
页码:25 / 29
页数:5
相关论文
共 50 条
  • [31] Classification of operator behaviors using a self-organizing map and ontology
    Kidokoro, Takuya
    Suzuki, Satoshi
    Igarashi, Hiroshi
    Nakata, Syuichi
    Kobayashi, Harumi
    Yasuda, Tetsuya
    [J]. IEEJ Transactions on Industry Applications, 2013, 133 (03) : 300 - 306
  • [32] Image retrieval using hierarchical self-organizing feature maps
    Sethi, IK
    Coman, I
    [J]. PATTERN RECOGNITION LETTERS, 1999, 20 (11-13) : 1337 - 1345
  • [33] Solving classification problems using supervised self-organizing map
    Thammano, Arft
    Kiatwuthiamorn, Jirapom
    [J]. 2007 IEEE INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND INFORMATION TECHNOLOGY, VOLS 1-3, 2007, : 236 - 239
  • [34] Comparative Study of Self-Organizing Map and Deep Self-Organizing Map using MATLAB
    Kumar, Indra D.
    Kounte, Manjunath R.
    [J]. 2016 INTERNATIONAL CONFERENCE ON COMMUNICATION AND SIGNAL PROCESSING (ICCSP), VOL. 1, 2016, : 1020 - 1023
  • [35] Multiscale image segmentation using a hierarchical self-organizing map
    Bhandarkar, SM
    Koh, J
    Suk, M
    [J]. NEUROCOMPUTING, 1997, 14 (03) : 241 - 272
  • [36] LazySOM: Image Compression Using an Enhanced Self-Organizing Map
    Tsai, Cheng-Fa
    Lin, Yu-Jiun
    [J]. ADVANCES IN IMAGE AND VIDEO TECHNOLOGY, PROCEEDINGS, 2009, 5414 : 118 - 129
  • [37] NOISY IMAGE SEGMENTATION USING A SELF-ORGANIZING MAP NETWORK
    Gorjizadeh, Saleh
    Pasban, Sadegh
    Alipour, Siavash
    [J]. ADVANCES IN SCIENCE AND TECHNOLOGY-RESEARCH JOURNAL, 2015, 9 (26) : 118 - 123
  • [38] Impact perforation image processing using a self-organizing map
    Okubo, Kenji
    Ogawa, Takehiko
    Kanada, Hajime
    [J]. PROCEEDINGS OF SICE ANNUAL CONFERENCE, VOLS 1-8, 2007, : 1095 - 1099
  • [39] Color image segmentation using a self-organizing map algorithm
    Huang, HY
    Chen, YS
    Hsu, WH
    [J]. JOURNAL OF ELECTRONIC IMAGING, 2002, 11 (02) : 136 - 148
  • [40] Deep Self-Organizing Map for Visual Classification
    Liu, Nan
    Wang, Jinjun
    Gong, Yihong
    [J]. 2015 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2015,