shared feature extraction for semi-supervised image classification
Multi-task learning (MTL) plays an important role in image analysis applications, e.g. image classification, face recognition and image annotation. That is because MTL can estimate the latent shared subspace to represent the common features given a set of images from different tasks. However, the geometry of the data probability distribution is always supported on an intrinsic image sub-manifold that is embedded in a high dimensional Euclidean space. Therefore, it is improper to directly apply MTL to multiclass image classification. In this paper, we propose a manifold regularized MTL (MRMTL) algorithm to discover the latent shared subspace by treating the high-dimensional image space as a sub-manifold embedded in an ambient space. We conduct experiments on the PASCAL VOC'07 dataset with 20 classes and the MIR dataset with 38 classes by comparing MRMTL with conventional MTL and several representative image classification algorithms. The results suggest that MRMTL can properly extract the common features for image representation and thus improve the generalization performance of the image classification models.
semi-supervised image classification multi-task learning manifold regularization