Classification of Instagram photos: topic modelling vs transfer learning

被引:0
作者
Tsapatsoulis, Nicolas [1 ]
机构
[1] Cyprus Univ Technol, Limassol, Cyprus
来源
PROCEEDINGS OF THE 12TH HELLENIC CONFERENCE ON ARTIFICIAL INTELLIGENCE, SETN 2022 | 2022年
关键词
image classification; topic modelling; transfer learning; deep learning;
D O I
10.1145/3549737.3549759
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The existence of pre-trained deep learning models for image classification, such as those trained on the well-known Resnet-50 architecture, allows for easy application of transfer learning to several domains including image retrieval. Recently, we proposed topic modelling for the retrieval of Instagram photos based on the associated hashtags. In this paper we compare content-based image classification, based on transfer learning, with the classification based on topic modelling of Instagram hashtags for a set of 24 different concepts. The comparison was performed on a set of 1944 Instagram photos, 81 per concept. Despite the excellent performance of the pre-trained deep learning models, it appears that text-based retrieval, as performed by the topic models of Instagram hashtags, stills perform better.
引用
收藏
页数:7
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