Category-Oriented Self-Learning Graph Embedding for Efficient Image Compact Representation

Abstract

As one of the ways to acquire 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 intended 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 the class but excluding the affinity of different classes, which results in the inability to fully capture the marginal structure between distributions of different classes. To overcome these issues, we propose a learning framework that implements Category-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 easily be extended to the semi-supervised situation. Extensive experiments on several widely-used benchmark databases demonstrate the effectiveness of the proposed method comparing with some state-of-the-art approaches.