AID-AppEAL: Automatic Image Dataset and Algorithm for Content Appeal Enhancement and Assessment Labeling
<|reference_start|>AID-AppEAL: Automatic Image Dataset and Algorithm for Content Appeal Enhancement and Assessment Labeling: We propose Image Content Appeal Assessment (ICAA), a novel metric that quantifies the level of positive interest an image's content generates for viewers, such as the appeal of food in a photograph. This is fundamentally different from traditional Image-Aesthetics Assessment (IAA), which judges an image's artistic quality. While previous studies often confuse the concepts of ``aesthetics'' and ``appeal,'' our work addresses this by being the first to study ICAA explicitly. To do this, we propose a novel system that automates dataset creation and implements algorithms to estimate and boost content appeal. We use our pipeline to generate two large-scale datasets (70K+ images each) in diverse domains (food and room interior design) to train our models, which revealed little correlation between content appeal and aesthetics. Our user study, with more than 76% of participants preferring the appeal-enhanced images, confirms that our appeal ratings accurately reflect user preferences, establishing ICAA as a unique evaluative criterion. Our code and datasets are available at https://github.com/SherryXTChen/AID-Appeal.<|reference_end|>
@article{chen2024aid-appeal:,
    title={AID-AppEAL: Automatic Image Dataset and Algorithm for Content Appeal
  Enhancement and Assessment Labeling},
    author={Sherry X. Chen, Yaron Vaxman, Elad Ben Baruch, David Asulin, Aviad
  Moreshet, Misha Sra, Pradeep Sen},
    journal={arXiv preprint arXiv:2407.05546},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.05546},
    primaryClass={cs.CV}
}