Action Recognition via Adaptive Semi-Supervised Feature Analysis

This study presents a new semi-supervised action recognition method via adaptive feature analysis. We assume that videos can be regarded as data points in embedding manifold subspace, and their matching problem quantified through specific Grassmannian kernel function while integrating correlation exploration similarity measurement into joint framework. By maximizing the intra-class compactness based on labeled data, our algorithm learn multiple features leverage unlabeled to enhance recognition. introduce kernels Projected Barzilai–Borwein (PBB) train subspace projection matrix classifier. Experiment results show has outperformed compared approaches when few training samples are available.