Abstract
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.
Keywords
Computer scienceDiscriminative modelCategorical variableAffordanceArtificial intelligenceTaxonomy (biology)Variety (cybernetics)DatabaseInformation retrievalMachine learningHuman–computer interaction
Affiliated Institutions
Related Publications
SUN database: Large-scale scene recognition from abbey to zoo
Scene categorization is a fundamental problem in computer vision. However, scene understanding research has been constrained by the limited scope of currently-used databases whi...
ConceptLearner: Discovering visual concepts from weakly labeled image collections
Discovering visual knowledge from weakly labeled data is crucial to scale up computer vision recognition systems, since it is expensive to obtain fully labeled data for a large ...
Unsupervised Feature Learning via Non-parametric Instance Discrimination
Neural net classifiers trained on data with annotated class labels can also capture apparent visual similarity among categories without being directed to do so. We study whether...
What, where and who? Classifying events by scene and object recognition
We propose a first attempt to classify events in static images by integrating scene and object categorizations. We define an event in a static image as a human activity taking p...
Emerging Properties in Self-Supervised Vision Transformers
In this paper, we question if self-supervised learning provides new properties to Vision Transformer (ViT) that stand out compared to convolutional networks (convnets). Beyond t...
Publication Info
- Year
- 2012
- Type
- article
- Pages
- 2751-2758
- Citations
- 857
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
857
OpenAlex
Cite This
Geneviève Patterson,
James Hays
(2012).
SUN attribute database: Discovering, annotating, and recognizing scene attributes.
, 2751-2758.
https://doi.org/10.1109/cvpr.2012.6247998
Identifiers
- DOI
- 10.1109/cvpr.2012.6247998