Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Scene-Centered Description from Spatial Envelope Properties
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated 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
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
A Hierarchical GIST Model Embedding Multiple Biological Feasibilities for Scene Classification
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Exploiting Textons distributions on spatial hierarchy for scene classification
Journal on Image and Video Processing - Special issue on selected papers from multimedia modeling conference 2009
CENTRIST: A Visual Descriptor for Scene Categorization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Building global image features for scene recognition
Pattern Recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Scene classification using a multi-resolution bag-of-features model
Pattern Recognition
Instant scene recognition on mobile platform
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
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Scene classification has been the target of much research. Most psychological studies have agreed that humans perceive a scene first globally recognizing its category and then they localize and recognize objects. In previous work the same feature set were used in classifying both natural scenes and manmade scenes simultaneously. We suggest the use of different features for each. In this paper the proposed features for natural scenes classification are presented. The new proposed features are inspired from the way humans perceive and recognize scenes at a glance. Outdoor scenes global features such as openness, roughness, and dominant directions have been investigated and translated into a new feature set, focusing on characteristics that efficiently differentiate between natural scene sub-classes. The effectiveness of the proposed features is tested using two datasets consists of 4 natural scenes (coast, mountain, forest, and open country) and 6 natural scenes (the previous 4 scenes plus desert and waterfall scenes), the first dataset is a benchmark data set used for testing scene classification techniques. Results showed that a classification accuracy of up to 95% could be achieved using the proposed feature set.