Class-Oriented Self-Learning Graph Embedding for Image Compact Representation

As one of the learning ways for inducing efficient image compact representation, graph embedding (GE) based manifold learning has been widely developed over the last two decades. Good graph embedding depends on the construction of graphs concerning intra-class compactness and inter-class separability, which are crucial indicators of the effectiveness of a model in generating discriminative features. Unsupervised approaches are designed to reveal the data structure information from a local or global perspective, but the resulting compact representation often has poorly inter-class margins due to the lack of label information. Moreover, supervised techniques only consider enhancing the adjacency affinity within classes, but exclude the affinity of different classes, resulting in inadequate capture of marginal structures between different class distributions. To overcome these issues, we propose a learning framework that implements Class-Oriented Self-Learning Graph Embedding (COSLGE), in which we achieve a flexible low-dimensional compact representation by imposing an adaptive graph learning process across the entire data while examining the inter-class separability of low-dimensional embedding by jointly learning a linear classifier. Besides, our framework can be easily extended to semi-supervised scenarios. Extensive experiments on several widely-used benchmark databases demonstrate the effectiveness of the proposed method in comparison to some state-of-the-art approaches.