superpixel

Superpixel refers to a perceptually meaningful group of adjacent pixels that share similar color, intensity, and texture features, forming a homogeneous region in an image. By aggregating pixels into superpixels, an image can be represented with fewer elements than every individual pixel, aiding subsequent analysis while roughly preserving object boundaries.

The most widely used methods are based on clustering in a multi-dimensional feature space. SLIC, or Simple Linear Iterative Clustering, performs k-means clustering in a five-dimensional space consisting of color (for example Lab) and spatial coordinates, with a compactness parameter controlling boundary adherence. Other approaches include SEEDS, which updates seed regions iteratively using histograms; and graph-based methods such as the Felzenszwalb– Huttenlocher algorithm and Quickshift, which form regions by optimizing local similarity or density estimates.

Properties: The number of superpixels is typically fixed in advance, providing a trade-off between detail and efficiency. Superpixels tend to align with image edges but may cut across textured regions; shapes are generally irregular but locally compact. They serve as a balance between pixel-level detail and region-level abstraction.

Applications: They are widely used as a preprocessing step for object and scene segmentation, texture and color analysis, optical flow, and depth estimation. In deep learning, networks may operate on superpixel-aggregated representations to reduce computation or improve boundary preservation.

Limitations: Sensitive to parameter choices, can produce over- or under-segmentation, and may not perfectly align with object boundaries under complex textures. Evaluation commonly considers boundary recall and under-segmentation error.