Transient attributes for high-level understanding and editing of outdoor scenes

This work studies "transient scene attributes" -- high level properties which affect scene appearance, such as "snow", "autumn", "dusk", "fog", and defines 40 transient attributes and uses crowdsourcing to annotate thousands of images from 101 webcams to train regressors that can predict the presence of attributes in novel images.

Abstract

We live in a dynamic visual world where the appearance of scenes changes dramatically from hour to hour or season to season. In this work we study "transient scene attributes" -- high level properties which affect scene appearance, such as "snow", "autumn", "dusk", "fog". We define 40 transient attributes and use crowdsourcing to annotate thousands of images from 101 webcams. We use this "transient attribute database" to train regressors that can predict the presence of attributes in novel images. We demonstrate a photo organization method based on predicted attributes. Finally we propose a high-level image editing method which allows a user to adjust the attributes of a scene, e.g. change a scene to be "snowy" or "sunset". To support attribute manipulation we introduce a novel appearance transfer technique which is simple and fast yet competitive with the state-of-the-art. We show that we can convincingly modify many transient attributes in outdoor scenes.