Images of scenes have various objects as well as abundant attributes, and diverse levels of visual categorization are possible. A natural image could be assigned with finegrained labels that describe major components, coarsegrained labels that depict high level abstraction, or a set of labels that reveal attributes. Such categorization at different concept layers can be modeled with label graphs encoding label information. In this paper, we exploit this rich information with a state-of-art deep learning framework, and propose a generic structured model that leverages diverse label relations to improve image classification performance. Our approach employs a novel stacked label prediction neural network, capturing both inter-level and intra-level label semantics. We evaluate our method on benchmark image datasets, and empirical results illustrate the efficacy of our model.
Finally, Hu et al. REF observe that diverse levels of visual categorization are possible depending on the level of desired abstraction.
Learning Structured Inference Neural Networks with Label Relations
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Computer Science