April 24, 2017
Taxonomy augmented object recognition
- Ji Q.
- Wang X.
- Yue Zhao
Realistic scene object recognition in computer vision still faces great challenges due to the large intra-class variation of object images caused by factors like object appearance variation and viewpoint change. To address this challenge, we propose to exploit the semantic relationships embedded in object taxonomy for improved object recognition. Specifically, we exploit the relationships in the object taxonomy to augment the learning of object classifiers. We utilize two types of relationships in the taxonomy, including the overall relationship and the local relationship. Our proposed approach jointly incorporates both the overall relationship as the loss function for classifier learning, and the local relationship as classifier learning constraints. Experiments on benchmark datasets demonstrate the effectiveness of our method in incorporating taxonomy for object recognition compared to the state-art-the-art methods.View Original Article