@InProceedings{8902803,
  author = {E. Berson and N. Q. K. Duong and C. Demarty},
  booktitle = {2019 27th European Signal Processing Conference (EUSIPCO)},
  title = {Collecting, Analyzing and Predicting Socially-Driven Image Interestingness},
  year = {2019},
  pages = {1-5},
  abstract = {Interestingness has recently become an emerging concept for visual content assessment. However, understanding and predicting image interestingness remains challenging as its judgment is highly subjective and usually context-dependent. In addition, existing datasets are quite small for in-depth analysis. To push forward research in this topic, a large-scale interestingness dataset (images and their associated metadata) is described in this paper and released for public use. We then propose computational models based on deep learning to predict image interestingness. We show that exploiting relevant contextual information derived from social metadata could greatly improve the prediction results. Finally we discuss some key findings and potential research directions for this emerging topic.},
  keywords = {convolutional neural nets;image processing;learning (artificial intelligence);meta data;social networking (online);visual content assessment;large-scale interestingness dataset;social metadata;image interestingness;deep learning;Flickr;Feature extraction;Metadata;Computational modeling;Visualization;Semantics;Predictive models;Image interestingness;content and social interestingness;Flickr;LaFin dataset;contextual information;deep learning.},
  doi = {10.23919/EUSIPCO.2019.8902803},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2019/proceedings/papers/1570533561.pdf},
}

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In addition, existing datasets are quite small for in-depth analysis. To push forward research in this topic, a large-scale interestingness dataset (images and their associated metadata) is described in this paper and released for public use. We then propose computational models based on deep learning to predict image interestingness. We show that exploiting relevant contextual information derived from social metadata could greatly improve the prediction results. 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