Unsupervised and Semi-Supervised Image Classification With Weak Semantic Consistency

Supervised methods have been widely used for image classifications. Although great progress has been made, existing supervised methods rely on well-labeled samples for classification. However, we often have large quantities of images with few or no labels. To cope with this problem, in this paper, we propose a novel weak semantic consistency constrained image classification method. We start from an extreme circumstance by viewing each image as one class. We train exemplar classifiers to separate each image from other images. For each image, we use the learned exemplar classifiers to predict the weak semantic correlations with the exemplar classifiers. When no labeled information is available, we cluster images using the weak semantic correlations and assign images within one cluster to the same mid-level class. When partially labeled images are available, we can use them to constrain the clustering process by assigning images of varied semantics to different mid-level classes. We use the newly assigned images for classifier training and new image representations, which can then be used for similar image assignments. The classifier training, image representation, and assignment processes are repeated until convergence. We conduct both unsupervised and semi-supervised image classification experiments on several datasets. The experimental results show the effectiveness of the proposed unsupervised and semi-supervised weak semantic consistency image classification method.