Square Superpixel Generation and Representation Learning via Granular-ball Computing

Superpixels, known for their advantages in reducing computational complexity and preserving image boundaries, have been widely utilized in the visual domain. However, existing superpixel generation methods typically produce irregularly shaped superpixels, which limit their adaptability to convolutional learning. Consequently, most superpixel generation methods are confined to being independent preprocessing steps, further hindering parallel processing and end-to-end training of images. Inspired by the adaptive representation and coverage capabilities of the granular-ball computing, we propose a square superpixel generation method. Specifically, this approach approximates superpixels using square blocks of varying sizes, effectively overcoming the computational limitations imposed by irregular shapes, thereby enabling parallelism and learnable features. Additionally, the method calculates the intensity similarity of pixels to generate a purity score for each square block, using these scores to select optimal superpixel blocks. Furthermore, we integrate these superpixel blocks as nodes in graph neural networks and tokens in Vision Transformers, enabling the effective extraction of multi-scale information and structured representation of visual features. Experimental results demonstrate that this method significantly enhances computational efficiency and model performance in downstream tasks, validating the effectiveness of the square superpixel generation approach.