Integrating multi-level deep learning and concept ontology for large-scale visual recognition

Abstract

To support large-scale visual recognition (i.e., recognizing thousands or even tens of thousands of object classes), a multi-level deep learning algorithm is developed to learn multiple deep networks and a tree classifier jointly, where a concept ontology is constructed to organize large numbers of object classes hierarchically in a coarse-to-fine fashion and determine the inter-related learning tasks automatically. Our multi-level deep learning algorithm can: (a) train multiple deep networks simultaneously to achieve more discriminative representations of both coarse-grained groups and fine-grained object classes at different levels of the concept ontology (i.e., learning multiple sets of deep features simultaneously for different tasks); (b) leverage multi-task learning to train more discriminative classifiers for the fine-grained object classes in the same group to enhance their separability significantly and enable inter-class knowledge transferring; and (c) learn multiple deep networks and the tree classifier jointly in an end-to-end fashion. Our experimental results on three image sets have demonstrated that our multi-level deep learning algorithm can achieve very competitive results on both the accuracy rates and the computational efficiency for large-scale visual recognition.