Property-Specific Aesthetic Assessment With Unsupervised Aesthetic Property Discovery

Abstract

We propose the property-specific aesthetic assessment (PSAA) algorithm with unsupervised aesthetic property discovery. The proposed PSAA algorithm uses an aesthetic feature extractor, an aesthetic property classifier, and multiple property-specific assessment networks. The aesthetic feature extractor analyzes aesthetics of images to generate features. Using such aesthetic features, we discover diverse aesthetic properties in an unsupervised manner and develop the aesthetic property classifier to predict the aesthetic property of each image. For each discovered aesthetic property, we train a property-specific assessment network. Thus, we can assess the aesthetic quality of an image using the property-specific network that corresponds to its property. Experimental results on a large dataset show that the proposed PSAA algorithm achieves state-of-the-art aesthetic assessment performance. Furthermore, we demonstrate that PSAA is useful for improving aesthetic qualities of images in two applications: contrast enhancement and image cropping.