2016/01/16 by Wang, Zhangyang, Shiyu Chang, Chang, Shiyu +8
Computer Science · Neuroscience · #Aesthetic Perception and Analysis #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Neural and Evolutionary Computing (cs.NE) #Olfactory and Sensory Function Studies #Visual Attention and Saliency Detection
paper · pdf · doi:10.48550/arxiv.1601.04155
openalex publication_date 2016/01/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Image aesthetics assessment has been challenging due to its subjective nature. Inspired by the scientific advances in the human visual perception and neuroaesthetics, we design Brain-Inspired Deep Networks (BDN) for this task. BDN first learns attributes through the parallel supervised pathways, on a variety of selected feature dimensions. A high-level synthesis network is trained to associate and transform those attributes into the overall aesthetics rating. We then extend BDN to predicting the distribution of human ratings, since aesthetics ratings are often subjective. Another highlight is our first-of-its-kind study of label-preserving transformations in the context of aesthetics assessment, which leads to an effective data augmentation approach. Experimental results on the AVA dataset show that our biological inspired and task-specific BDN model gains significantly performance improvement, compared to other state-of-the-art models with the same or higher parameter capacity.