Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
The Journal of Machine Learning Research
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
Modeling Scenes with Local Descriptors and Latent Aspects
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
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
International Journal of Computer Vision
Review: Which is the best way to organize/classify images by content?
Image and Vision Computing
Universal and Adapted Vocabularies for Generic Visual Categorization
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Heterogeneous bag-of-features for object/scene recognition
Applied Soft Computing
Hi-index | 12.05 |
Natural scene classification (NSC) is a challenging pattern classification problem. As one of state-of-the-art techniques, the bag-of-feature (BOF) model has received extensive considerations in characterizing the image. To boost the flexibility during visterm construction in BOF model, an integrated scheme for image representation is proposed by adaptive analysis on the local visual complexity of image itself. First, the flatness of each scene category is determined by the total flatness of all images belonging to this category. Then the new integrated image representation of the scene category is built by weighting the two representations (based on a pixels gray value descriptor and a dense SIFT descriptor) through the normalized coefficients computed by the flatness of the category. Finally, a hierarchical generative model is exploited to learn natural scene categories. Experimental results demonstrate that the satisfactory classification accuracy achieves about 83.67% on a large set of 15 categories of complex scenes.