An automatic segmentation technique for color images based on SOFM neural network

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3 Citations (Scopus)

Abstract

In this paper, an automatic segmentation method based on self-organizing feature map (SOFM) neural network (NN) is presented for color images. First, a binary tree clustering procedure is used to cluster the colors in an image. In each node of the tree, a SOFM NN is used as a classifier which is fed by image color values. The output neurons of the SOFM NN define the color classes for each node. In our method, the number of color classes for each node is two. For each node of the tree, Hotelling transform based splitting condition is used to define if the current color classes should be split. To speed up the entire algorithm, a nearest neighbor interpolation is used to get the small training set for SOFM NN. Once the colors in an image are clustered, it is easy to segment a target by analyzing the color feature in an image. The method is independent of the color scheme, so it is applicable to any type of color images. Our experimental results show the validity of the proposed method.

Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
Pages3528-3533
Number of pages6
DOIs
Publication statusPublished - 2009
Event2009 International Joint Conference on Neural Networks, IJCNN 2009 - Atlanta, GA
Duration: 2009 Jun 142009 Jun 19

Other

Other2009 International Joint Conference on Neural Networks, IJCNN 2009
CityAtlanta, GA
Period09/6/1409/6/19

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ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Cite this

Zhang, J., & Furuzuki, T. (2009). An automatic segmentation technique for color images based on SOFM neural network. In Proceedings of the International Joint Conference on Neural Networks (pp. 3528-3533). [5178725] https://doi.org/10.1109/IJCNN.2009.5178725