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].