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<p>This article discusses a topology adaptive self-organizing circular neural network (TASOCNN) that can be used to cluster circular data. TASOCNN consists of a set of interconnected processors that are trained to capture the topological structure of the circular data. The connections (edges) between the processors are assigned strength values which are updated during the training process. However, the naive TASOCNN model has some limitations, such as its inability to accurately distinguish between inter-cluster and intra-cluster edges. The article proposes significant modifications to naive TASOCNN, including updates to strength equations for generating stronger and shorter edges in dense areas of data. Additionally, a novel non-iterative clustering procedure based on a two-component Beta mixture model is introduced. This model isolates intra-cluster edges by analyzing strength values, leading to the removal of inter-cluster edges. The application of TASOCNN is demonstrated in the context of color image segmentation, a crucial task in various applications. The results show that the proposed method outperforms the naive TASOCNN model and other state-of-the-art self-organizing neural models as well as mixture models.</p>

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Enhancing clustering performance for TASOCNN: a topology adaptive approach for circular data with application to color image segmentation

  • 期刊论文

  • 发表日期：

    2026年2月26日

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  • Anandarup Roy,
  • Oendrila Samanta

摘要

This article discusses a topology adaptive self-organizing circular neural network (TASOCNN) that can be used to cluster circular data. TASOCNN consists of a set of interconnected processors that are trained to capture the topological structure of the circular data. The connections (edges) between the processors are assigned strength values which are updated during the training process. However, the naive TASOCNN model has some limitations, such as its inability to accurately distinguish between inter-cluster and intra-cluster edges. The article proposes significant modifications to naive TASOCNN, including updates to strength equations for generating stronger and shorter edges in dense areas of data. Additionally, a novel non-iterative clustering procedure based on a two-component Beta mixture model is introduced. This model isolates intra-cluster edges by analyzing strength values, leading to the removal of inter-cluster edges. The application of TASOCNN is demonstrated in the context of color image segmentation, a crucial task in various applications. The results show that the proposed method outperforms the naive TASOCNN model and other state-of-the-art self-organizing neural models as well as mixture models.