Research on the optimization of simple linear iterative clustering algorithm in superpixel segmentation

Superpixel-based image segmentation methods have gained significant attention due to their ability to reduce computational complexity while effectively preserving image boundary information. These methods have evolved over time, transitioning from traditional clustering and graph-based techniques to more advanced approaches, including those enhanced by deep learning models. Superpixels, which are groups of pixels that share similar attributes, provide a compact and efficient representation of an image, allowing for improved processing in various computer vision tasks. In this paper, we present an improved version of the Simple Linear Iterative Clustering (SLIC) algorithm, referred to as MSLIC (Modified SLIC). The MSLIC algorithm integrates boundary saliency measurement and region-adaptive adjustments, significantly enhancing both segmentation accuracy and efficiency. The boundary saliency measurement helps in better preserving the boundaries of objects, while region-adaptive adjustments ensure that different regions are segmented more accurately, especially in complex images. To evaluate the effectiveness of the proposed method, extensive experiments were conducted on the Berkeley Segmentation Dataset 500 (BSDS500), a well-known benchmark for segmentation performance. The results show that the MSLIC algorithm outperforms traditional methods in terms of boundary recall, undersegmentation error, and runtime efficiency. The proposed method achieves a better trade-off between segmentation quality and computational cost, making it highly suitable for real-time applications and scenarios requiring high precision. The findings of this study provide valuable insights into the development of robust, efficient, and scalable segmentation techniques, with applications in areas such as object recognition, image editing, medical imaging, and autonomous driving.