Efficient Discriminative Local Learning for Object Recognition

Abstract

Although object recognition methods based on local learning can reasonably resolve the difficulties caused by the large variations in images from the same category, the high risk of overfitting and the heavy computational cost in training numerous local models (classifiers or distance functions) often limit their applicability. To address these two unpleasant issues, we cast the multiple, independent training processes of local models as a correlative multi-task learning problem, and design a new boosting algorithm to accomplish it. Specifically, we establish a parametric space where these local models lie and spread as a manifold-like structure, and use boosting to perform local model training by completing the manifold embedding. Via sharing the common embedding space, the learning of each local model can be properly regularized by the extra knowledge from other models, while the training time is also significantly reduced. Experimental results on two benchmark datasets, Caltech-101 and VOC 2007, support that our approach not only achieves promising recognition rates but also gives a two order speed-up in realizing local learning.