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In the context of the increasing need for accurate image analysis at the pixel level, the problem of semantic image segmentation has been posed to separate different meaningful regions without the need for labeling data. An unsupervised learning method has been applied, in which the boundary information is enriched with an energy distance map and then highlights are detected through the Points of Interest (PoI) technique to improve boundary discrimination. The model is carried out through K-means clustering in normalized characteristic space, combining energy characteristics and POI coordinates to increase accuracy in complex regions. The experimental results have shown that the semantic regions are more clearly separated, the number of POIs is satisfactory, and the overall accuracy is improved compared to the method of using only basic characteristic clustering.

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Towards Robust Semantic Segmentation: A Non-Deep Learning Dominant Approach with Energy Distance and Point of Interest

  • 会议论文

  • 发表日期：

    2026年

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  • Toan Phung Huynh,
  • Tai Van Vo,
  • Hiep Xuan Huynh

摘要

In the context of the increasing need for accurate image analysis at the pixel level, the problem of semantic image segmentation has been posed to separate different meaningful regions without the need for labeling data. An unsupervised learning method has been applied, in which the boundary information is enriched with an energy distance map and then highlights are detected through the Points of Interest (PoI) technique to improve boundary discrimination. The model is carried out through K-means clustering in normalized characteristic space, combining energy characteristics and POI coordinates to increase accuracy in complex regions. The experimental results have shown that the semantic regions are more clearly separated, the number of POIs is satisfactory, and the overall accuracy is improved compared to the method of using only basic characteristic clustering.