NDLI: TASOCNN: a topology adaptive self-organizing circular neural network and its application to color segmentation

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TASOCNN: a topology adaptive self-organizing circular neural network and its application to color segmentation

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TASOCNN: a topology adaptive self-organizing circular neural network and its application to color segmentation

Content Provider	ACM Digital Library
Author	Roy, Utpal Parui, Swapan Kumar Roy, Anandarup
Abstract	Topology adaptive neural networks are popular because of its capability to adopt the underlaying topology of data. In this paper we develop a topology adaptive self organizing circular neural network (TASOCNN) model for circular-linear data sampled from an unit disk. The basic framework uses the TASONN procedure of Datta et. al. [8]. The update rules and the distance measure are reformulated with the inclusion of directional information. In the iterative TASOCNN process, we create/update edges between any two winner processors. These edges are weighted, hence finally a weighted processor graph is created concerning processors as vertices. By removing possible inter-cluster edges from the connected components of this processor graph, a set of subgraphs can be obtained. For this purpose we use a cost function based on edge length and strength. Many of these subgraphs become close to one another. These close subgraphs are merged. Finally, each subgraph represents a cluster in the data. We apply TASOCNN for color based image segmentation. TASOCNN is constructed in the hue-saturation space. The Berkeley segmentation dataset is used to present the results. An evaluation is made by means of probabilistic rand index. Our experiments reveals satisfactory outcome of TASOCNN.
Starting Page	427
Ending Page	434
Page Count	8
File Format	PDF
ISBN	9781450300605
DOI	10.1145/1924559.1924616
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2010-12-12
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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