Latent Factors of Visual Popularity Prediction

The ICMR2015 paper entitled Latent Factors of Visual Popularity Prediction by Spencer Cappallo and Thomas Mensink and Cees G. M. Snoek is now available. Predicting the popularity of an image on social networks based solely on its visual content is a difficult problem. One image may become widely distributed and repeatedly shared, while another similar image may be totally overlooked. We aim to gain insight into how visual content affects image popularity. We propose a latent ranking approach that takes into account not only the distinctive visual cues in popular images, but also those in unpopular images. This method is evaluated on two existing datasets collected from photo-sharing websites, as well as a new proposed dataset of images from the microblogging website Twitter. Our experiments investigate factors of the ranking model, the level of user engagement in scoring popularity, and whether the discovered senses are meaningful. The proposed approach yields state of the art results, and allows for insight into the semantics of image popularity on social networks.

This entry was posted in Science. Bookmark the permalink.

2 Responses to Latent Factors of Visual Popularity Prediction

  1. Kyle says:

    Hey I have read our paper and I have a question. What are the actual visual features that are used to predict popularity? For example, is the the location of objects, or semantic meaning of images?

    Best regards,
    K

  2. Cees says:

    Pls contact Spencer Cappallo at s.h.cappallo AT uva DOT nl
    -Cees.

Leave a Reply

Your email address will not be published. Required fields are marked *