Joint Image and Text Representation for Aesthetics Analysis

Abstract

Image aesthetics assessment is essential to multimedia applications such as image retrieval, and personalized image search and recommendation. Primarily relying on visual information and manually-supplied ratings, previous studies in this area have not adequately utilized higher-level semantic information. We incorporate additional textual phrases from user comments to jointly represent image aesthetics utilizing multimodal Deep Boltzmann Machine. Given an image, without requiring any associated user comments, the proposed algorithm automatically infers the joint representation and predicts the aesthetics category of the image. We construct the AVA-Comments dataset to systematically evaluate the performance of the proposed algorithm. Experimental results indicate that the proposed joint representation improves the performance of aesthetics assessment on the benchmarking AVA dataset, comparing with only visual features.