A hierarchical neural network and its application to image segmentation

Suchendra M. Bhandarkar, Jean Koh and Minsoo Suk

Mathematics and Computers in Simulation (MATCOM), 1996, vol. 41, issue 3, 337-355

Abstract: The problem of image segmentation can be formulated as one of vector quantization. Although self-organizing networks with competitive learning are useful for vector quantization, they, in their original single-layer structure, are inadequate for image segmentation. This paper proposes and describes a hierarchical self-organizing neural network for image segmentation. The hierarchical self-organizing feature map (HSOFM) which is an extension of the traditional (single-layer) self-organizing feature map (SOFM) is seen to alleviate the shortcomings of the latter in the context of image segmentation. The problem of image segmentation is formulated as one of vector quantization and mapped onto the HSOFM. The HSOFM combines the ideas of self-organization and topographic mapping with those of multi-scale image segmentation. Experimental results using intensity and range images bring out the advantages of the HSOFM over the conventional SOFM.

Keywords: Image segmentation; Self-organizing feature map; Neural networks; Computer vision (search for similar items in EconPapers)
Date: 1996
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/0378475495000836
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:41:y:1996:i:3:p:337-355

DOI: 10.1016/0378-4754(95)00083-6

Access Statistics for this article

Mathematics and Computers in Simulation (MATCOM) is currently edited by Robert Beauwens

More articles in Mathematics and Computers in Simulation (MATCOM) from Elsevier
Bibliographic data for series maintained by Catherine Liu ().