Journal
PATTERN RECOGNITION
Volume 78, Issue -, Pages 198-214Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2018.01.027
Keywords
Large-scale visual recognition; Multi-level deep learning; Multiple deep networks; Concept ontology; Multi-task learning; Tree classifier
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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. (C) 2018 Elsevier Ltd. All rights reserved.
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