A fast method for estimating transient scene attributes

We propose the use of deep convolutional neural networks to estimate the transient attributes of a scene from a single image. Transient scene attributes describe both the objective conditions, such as the weather, time of day, and the season, and subjective properties of a scene, such as whether or not the scene seems busy. Recently, convolutional neural networks have been used to achieve state-of-the-art results for many vision problems, from object detection to scene classification, but have not previously been used for estimating transient attributes. We compare several methods for adapting an existing network architecture and present state-of-the-art results on two benchmark datasets. Our method is more accurate and significantly faster than previous methods, enabling real-world applications.

1. Introduction

Outdoor scenes experience a wide range of lighting and weather conditions which dramatically affect their appearance. A scene can change from rainy and brooding to sunny and pleasant in a matter of hours, even minutes. The ability to quickly understand these fleeting, or transient, attributes is a critical skill that people often take for granted. Automatically understanding such subtle conditions has many potential applications, including: improving context-dependent anomaly detection [5]; enabling attribute-oriented browsing and search of large image sets[13], [29]; estimating micro-climate conditions using outdoor webcams [9]; as a pre-processing step for higher-level algorithms for calibration [12], [31], shape estimation [4], [32], geolocalization [14], [33]; and environmental monitoring [10].