Interaction-Matrix Based Personalized Image Aesthetics Assessment

Abstract

Personalized image aesthetics assessment (IAA) aims to estimate aesthetic experiences subject to the preferences of individual users, contrary to generic IAA that estimates aesthetic experiences subject to average preferences. Most existing personalized IAA methods treat personalized aesthetic experiences as deviations from a generic aesthetic experience, and therefore, personalized IAA models are designed to build upon the prior knowledge on generic IAA. However, we propose that acquiring knowledge on generic IAA is not necessary for building a personalized IAA model. Instead of modeling personalized IAA on the basis of generic IAA, this work proposes to directly estimate personalized aesthetic experiences from the interactions between image contents and user preferences (i.e., preference-content interaction), where interaction-matrices representing preference-content interactions are constructed without needs for prior generic IAA knowledge. To this end, we construct interaction-matrices from content features constructed from pre-trained image classification features and latent preference features. To realize a robust interaction-matrix based personalized IAA model, we discuss in detail on different strategies for constructing interaction-matrices and estimating personalized aesthetic scores from the interaction-matrices. Besides the personalized IAA scenario, we further propose strategies to adapt the proposed personalized IAA model to different scenarios of generic IAA. Extensive experiments show that: 1) our method significantly outperforms 5 previous relevant personalized IAA methods on FLICKR-AES dataset, especially the methods that require generic IAA knowledge as the basis; 2) in terms of generic IAA, the proposed approach also outperforms 13 generic IAA methods on AVA dataset.