Ryosuke Yamanishi, Yuki Mizoguchi, Yuji Iwahori: Construction of Attribute Dataset with SNS Mining for Generic Object Recognition, Proc. of the 25th International Conference on Knowledge Based and Intelligent Information and Engineering Systems, pp. 1401-1410, 2021年9月

This paper proposes a framework to automatically construct an attribute dataset with images and tags on the Social Network Service (SNS). In the field of general object recognition, attributes that represent characteristics and categories of objects (e.g., `cat’ has `white’ and `cute’ as its attributes) showed high effectiveness. In the existing study, the attributes have been prepared by researchers, and the costs of the annotation and the subjectivity of the annotator should be the issues to construct attribute dataset. In this paper, we verified the feasibility that tags on the SNS could be assumed the attributes. Based on the study, the proposed method automatically collects images and their corresponding attributes from the SNS; the attributes satisfy a)understandable for humans, b) understandable for computer, and c) available on multiple categories. The experiments confirmed the effectiveness of the attribute datasets constructed by the proposed method in the generic object recognition; each dataset from Instagram and Flicker showed 71.6% and 79.3% accuracy, respectively. It was suggested that the attribute datasets that the proposed method automatically constructed showed almost the same effectiveness as the human-created dataset.