Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
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
Higher order learning with graphs
ICML '06 Proceedings of the 23rd international conference on Machine learning
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
Context-based multi-label image annotation
Proceedings of the ACM International Conference on Image and Video Retrieval
Sampling strategies for bag-of-features image classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Learning descriptive visual representation by semantic regularized matrix factorization
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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This paper presents a novel framework that can combine latent semantic learning and reduced hypergraph learning for semi-supervised image categorization. To improve the traditional bag-of-features representation, we first propose a semantics-aware representation which can learn latent semantics automatically from a large vocabulary of abundant visual keywords through contextual spectral embedding. The learnt latent semantics can be readily used to define a histogram intersection kernel. Based on this semantics-aware kernel, we further develop a reduced hypergraph-based semi-supervised learning method to exploit both labeled and unlabeled images for image categorization. Experimental results have shown that the proposed framework can achieve significant improvements with respect to the state of the arts.