Incremental Spectral Clustering via Fastfood Features and Its Application to Stream Image Segmentation

摘要

We propose an incremental spectral clustering method for stream data clustering and apply it to stream image segmentation. The main idea in our work consists of generating the data points in the kernel space by Fastfood features and iteratively calculating the eigendecomposition of data. Compared with the popular Nystr?m-based approximation, our work accesses each data point only once while Nystr?m, in particular the sampling scheme, will go through the entire dataset first and calculate the embeddings of data points with a second visit. As a result, our method is able to learn data partitions incrementally and improve eigenvector approximation with more and more data seen from a stream. By contrast, the performance of the standard Nystr?m is fixed when the sample set is selected. Experimental results show the superiority of our method.