Understanding and Mapping Natural Beauty

TL;DR

This paper introduces a method to objectively quantify and predict the scenicness of outdoor images using a large crowdsourced dataset, enhancing image understanding and cross-view mapping.

Contribution

It presents a novel approach that accounts for human rating variance and integrates overhead images to improve scenicness prediction and related applications.

Findings

Quantitative scenicness measures improve semantic image understanding.

Method outperforms baseline approaches in scenicness prediction.

Cross-view mapping benefits from incorporating unlabeled overhead images.

Abstract

While natural beauty is often considered a subjective property of images, in this paper, we take an objective approach and provide methods for quantifying and predicting the scenicness of an image. Using a dataset containing hundreds of thousands of outdoor images captured throughout Great Britain with crowdsourced ratings of natural beauty, we propose an approach to predict scenicness which explicitly accounts for the variance of human ratings. We demonstrate that quantitative measures of scenicness can benefit semantic image understanding, content-aware image processing, and a novel application of cross-view mapping, where the sparsity of ground-level images can be addressed by incorporating unlabeled overhead images in the training and prediction steps. For each application, our methods for scenicness prediction result in quantitative and qualitative improvements over baseline approaches.