Modeling Image Appeal Based on Crowd Preferences for Automated Person-Centric Collage Creation

This paper attempts to model IA in personal photo collections through a user-centric perspective. To understand how users deemed an image as being more/less appealing, an extensive crowdsourcing experiment was conducted with 350 workers and five different albums. The significant variance in selection probabilities for the most and least appealing images indicated that images were not selected randomly, and there were underlying factors that influenced some images to be selected more often than others. We then employed nine low level image attributes to model the image selection process, and trained SVRs which could adequately predict image selections for the album-specific conditions. However, a generic SVR failed to model the selection patterns as adequately as the album-specific SVRs suggesting that context greatly influences the categorization of what is more and less appealing. Experimental results demonstrate that our approach is promising. However, more attributes (related to image semantics) are needed to accurately model image selection characteristics.