Relevance learning for spectral clustering with applications on image segmentation and video behaviour profiling

We aim to tackle the problem of unsupervised visual learning. A novel relevance learning algorithm is proposed for data clustering using eigenvectors of a data affinity matrix. We show that it is critical to select the relevant eigenvectors for both estimating the optimal number of clusters and performing data clustering especially given noisy and sparse data. The effectiveness of our algorithm is demonstrated on solving two challenging visual data clustering problems: image segmentation and video behaviour profiling.