Knowledge Graph Representation Fusion Framework for Fine-Grained Object Recognition in Smart Cities

Yang He, Ling Tian, Lizong Zhang, Xi Zeng and Zhihan Lv

Complexity, 2021, vol. 2021, 1-9

Abstract: Autonomous object detection powered by cutting-edge artificial intelligent techniques has been an essential component for sustaining complex smart city systems. Fine-grained image classification focuses on recognizing subcategories of specific levels of images. As a result of the high similarity between images in the same category and the high dissimilarity in the same subcategories, it has always been a challenging problem in computer vision. Traditional approaches usually rely on exploring only the visual information in images. Therefore, this paper proposes a novel Knowledge Graph Representation Fusion (KGRF) framework to introduce prior knowledge into fine-grained image classification task. Specifically, the Graph Attention Network (GAT) is employed to learn the knowledge representation from the constructed knowledge graph modeling the categories-subcategories and subcategories-attributes associations. By introducing the Multimodal Compact Bilinear (MCB) module, the framework can fully integrate the knowledge representation and visual features for learning the high-level image features. Extensive experiments on the Caltech-UCSD Birds-200-2011 dataset verify the superiority of our proposed framework over several existing state-of-the-art methods.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:8041029

DOI: 10.1155/2021/8041029

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