Basic level scene understanding: from labels to structure and beyond
SIGGRAPH Asia 2012 Technical Briefs
Learning attribute-aware dictionary for image classification and search
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
Relative forest for attribute prediction
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Sentribute: image sentiment analysis from a mid-level perspective
Proceedings of the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining
GIANT: geo-informative attributes for location recognition and exploration
Proceedings of the 21st ACM international conference on Multimedia
Exploring outdoor appearance changes with transient scene attributes
SIGGRAPH Asia 2013 Posters
Proceedings of the 23rd international conference on World wide web
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In this paper we present the first large-scale scene attribute database. First, we perform crowd-sourced human studies to find a taxonomy of 102 discriminative attributes. Next, we build the “SUN attribute database” on top of the diverse SUN categorical database. Our attribute database spans more than 700 categories and 14,000 images and has potential for use in high-level scene understanding and fine-grained scene recognition. We use our dataset to train attribute classifiers and evaluate how well these relatively simple classifiers can recognize a variety of attributes related to materials, surface properties, lighting, functions and affordances, and spatial envelope properties.