Multiple-view flexible semi-supervised classification through consistent graph construction and label propagation

A unified semi-supervised model with joint estimation of graph, soft labels and latent subspace

Since manually labeling images is expensive and labor intensive, in practice we often do not have enough labeled images to train an effective classifier for the new image classification tasks. The graph-based SSL methods have received more attention in practice due to their convexity, scalability and efficiency. In this paper, we propose a novel graph-based semi-supervised learning method that takes full advantage of a small set of labeled graphs and a large set of unlabeled graph data. First, we explain the concept of graph-based semi-supervised learning. The core idea of these models is to jointly estimate a low-rank graph with soft labels and a latent subspace. The proposed scheme leverages the synergy between the graph structure and the data representation in terms of soft labels and latent features. This improves the monitoring information and leads to better discriminative linear transformation. Several experiments were conducted on five image datasets using state-of-the-art methods. These experiments show the effectiveness of the proposed semi-supervised method.

Multi-View Graph Fusion for Semi-Supervised Learning: Application to Image-Based Face Beauty Prediction

Facial Beauty Prediction (FBP) is an important visual recognition problem to evaluate the attractiveness of faces according to human perception. Most existing FBP methods are based on supervised solutions using geometric or deep features. Semi-supervised learning for FBP is an almost unexplored research area. In this work, we propose a graph-based semi-supervised method in which multiple graphs are constructed to find the appropriate graph representation of the face images (with and without scores). The proposed method combines both geometric and deep feature-based graphs to produce a high-level representation of face images instead of using a single face descriptor and also improves the discriminative ability of graph-based score propagation methods. In addition to the data graph, our proposed approach fuses an additional graph adaptively built on the predicted beauty values. Experimental results on the SCUTFBP-5500 facial beauty dataset demonstrate the superiority of the proposed algorithm compared to other state-of-the-art methods.