Real-Time Superpixel Segmentation by DBSCAN Clustering Algorithm

Abstract— A real-time image super-pixel segmentation method with 50 frames/s by using the density-based spatial clustering of applications with noise (DBSCAN) algorithm. In order to decrease the computational costs of superpixel algorithms, we adopt a fast two-step framework. In the first clustering stage, the DBSCAN algorithm with color-similarity and geometric restrictions is used to rapidly cluster the pixels, and then, small clusters are merged into superpixels by their neighborhood through a distance measurement defined by color and spatial features in the second merging stage. A robust and simple distance function is defined for obtaining better superpixels in these two steps. The experimental results demonstrate that our real-time superpixel algorithm (50 frames/s) by the DBSCAN clustering outperforms the state-of-the-art superpixel segmentation methods in terms of both accuracy and efficiency. A desired superpixel method needs to not only fulfil the requirement of good boundary adherence, but also be efficient. Since the superpixels are used as a preprocessing step in vision applications, the algorithm of high-quality superpixels with less computation is preferred. It is a density-based clustering algorithm. Since DBSCAN can find arbitrarily shaped clusters, it has a good potential to segment complex and irregularly shaped objects. In order to produce regular superpixles. < final year projects >