Superpixels for large dataset subspace clustering

Due to the numerous applications in computer vision, subspace clustering has been extensively studied in the past two decades. Most research puts emphasis on the spectral clustering-based methods in the recent years. This kind of methods usually extracts the affinity by the self-representation of the data points. Although they achieve the state-of-the-art results, the computation time will be unbearable when the number of the data points is large enough. In addition, the self-representation only considers the information provided by each single data point. In this paper, inspired by the idea of the superpixels in image segmentation, we first propose superpixels for subspace clustering with the large dataset. Then, we provide the strategy for the popular spectral clustering-based methods using these superpixels. Experimental results confirm that our superpixel-based subspace clustering methods can improve the computation speed dramatically. In addition, since the superpixels can consider the information provided by the group of data points, these methods can also improve the performance to some extent.