more "winter" more "night" more "warm" more "moist" more "rain" more "autumn" Figure 1: Our method enables high-level editing of outdoor photographs. In this example, the user provides an input image (left) and six attribute queries corresponding to the desired changes, such as more "autumn". Our method hallucinates six plausible versions of the scene with the desired attributes (right), by learning local color transforms from a large dataset of annotated outdoor webcams. AbstractWe 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.

We propose a hierarchical segmentation algorithm that starts with a very fine oversegmentation and gradually merges regions using a cascade of boundary classifiers. This approach allows the weights of region and boundary features to adapt to the segmentation scale at which they are applied. The stages of the cascade are trained sequentially, with asymetric loss to maximize boundary recall. On six segmentation data sets, our algorithm achieves best performance under most region-quality measures, and does it with fewer segments than the prior work. Our algorithm is also highly competitive in a dense oversegmentation (superpixel) regime under boundary-based measures.

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The amorphous TiO2 derived from hydroxylation has become an effective approach for the enhancement of photocatalytic activity of TiO2 since a kind of special black TiO2 was prepared by engineering disordered layers on TiO2 nanocrystals via hydrogenation. In this contribution, we prepared totally amorphous TiO2 with various degrees of blackness by introducing hydroxyls via ultrasonic irradiation, through which can we remarkably enhance the photocatalytic activity of TiO2 with improved light harvesting and narrowed band gap.