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 crowd-sourcing 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. Copyright © ACM.
Laffont, P. Y., Ren, Z., Tao, X., Qian, C., & Hays, J. (2014). Transient attributes for high-level understanding and editing of outdoor scenes. In ACM Transactions on Graphics (Vol. 33). Association for Computing Machinery. https://doi.org/10.1145/2601097.2601101