Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Contextual Priming for Object Detection
International Journal of Computer Vision
A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Categorization of natural scenes: local vs. global information
APGV '06 Proceedings of the 3rd symposium on Applied perception in graphics and visualization
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Rapid Biologically-Inspired Scene Classification Using Features Shared with Visual Attention
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scene Classification Using a Hybrid Generative/Discriminative Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Global-to-Local Oriented Rapid Scene Perception
WIAMIS '08 Proceedings of the 2008 Ninth International Workshop on Image Analysis for Multimedia Interactive Services
Scene categorization via contextual visual words
Pattern Recognition
Natural scene classification using overcomplete ICA
Pattern Recognition
Learning natural scene categories by selective multi-scale feature extraction
Image and Vision Computing
Novel Gaussianized vector representation for improved natural scene categorization
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Heterogeneous bag-of-features for object/scene recognition
Applied Soft Computing
Human-inspired features for natural scene classification
Pattern Recognition Letters
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In this paper, we present a simple, yet very efficient global image representation for scene recognition. A scene image is represented by a histogram of local transforms, which is an extended version of census transform histogram. The local transforms include local difference sign and magnitude information. Due to strong constraints between neighboring transformed values, global structure information can be captured through the histogram and spatial pyramid representation. Principal component analysis is used to reduce the dimensionality and get a compact feature vector. Experimental results on three widely used datasets demonstrate that the proposed method could achieve competitive performance in terms of speed and accuracy.