A Similarity Measure for Material Appearance We present a model to measure the similarity in appearance between different materials, which correlates with human similarity judgments. We first create a database of 9,000 rendered images depicting objects with varying materials, shape and illumination. We then gather data on perceived similarity from crowdsourced experiments; our analysis of over 114,840 answers suggests that indeed a shared perception of appearance similarity exists. We feed this data to a deep learning architecture with a novel loss function, which learns a feature space for materials that correlates with such perceived appearance similarity. Our evaluation shows that our model outperforms existing metrics. Last, we demonstrate several applications enabled by our metric, including appearancebased search for material suggestions, database visualization, clustering and summarization, and gamut mapping.

A Deeper Look at Human Visual Perception of Images How would one describe an image? Interesting? Pleasant? Aesthetic? A number of studies have classified images with respect to these attributes. A common approach is to link lower level image features with higher level properties, and train a computational model to perform classification using humanannotated ground truth. Although these studies generate algorithms with reasonable prediction performance, they provide few insights into why and how the algorithms work. The current study focuses on how multiple visual factors affect human perception of digital images. We extend an existing dataset with quantitative measures for human perception of 31 image attributes under 6 different viewing conditions: images that are intact, inverted, grayscale, inverted and grayscale, and images showing mainly low or highspatial frequency information. Statistical analyses indicate varying importance of holistic cues, color information, semantics, and saliency on different types of attributes. Building on these insights we build an empirical model of human image perception. Motivated by the empirical model, we designed computational models that predict highlevel image attributes. Extensive experiments demonstrate that understanding human visual perception helps create better computational models.