CENTRIST: A Visual Descriptor for Scene Categorization
Nanyang Technological University · Georgia Institute of Technology
Abstract
CENsus TRansform hISTogram (CENTRIST), a new visual descriptor for recognizing topological places or scene categories, is introduced in this paper. We show that place and scene recognition, especially for indoor environments, require its visual descriptor to possess properties that are different from other vision domains (e.g., object recognition). CENTRIST satisfies these properties and suits the place and scene recognition task. It is a holistic representation and has strong generalizability for category recognition. CENTRIST mainly encodes the structural properties within an image and suppresses detailed textural information. Our experiments demonstrate that CENTRIST outperforms the current state of the art…
Citation impact
- FWCI
- 43.97
- Percentile
- 100%
- References
- 44
Authors
2Topics & keywords
- Artificial intelligence
- Computer science
- Categorization
- Scale-invariant feature transform
- Histogram
- Cognitive neuroscience of visual object recognition
- Computer vision
- Scene statistics
- Sustainable cities and communities