Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Mapping a manifold of perceptual observations
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Document clustering based on non-negative matrix factorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
The Journal of Machine Learning Research
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
PLSA-based image auto-annotation: constraining the latent space
Proceedings of the 12th annual ACM international conference on Multimedia
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
Neighborhood Preserving Embedding
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
Modeling hidden topics on document manifold
Proceedings of the 17th ACM conference on Information and knowledge management
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic dyadic data analysis with local and global consistency
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Learning with l1-graph for image analysis
IEEE Transactions on Image Processing
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
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Image representation is the crucial component in image analysis and understanding. However, the widely used low-level features cannot correctly represent the high-level semantic content of images in many situations due to the "semantic gap". In order to bridge the "semantic gap", in this brief, we present a novel topic model, which can learn an effective and robust mid-level representation in the latent semantic space for image analysis. In our model, the ℓ1-graph is constructed to model the local image neighborhood structure and the word co-occurrence is computed to capture the local word consistency. Then, the local information is incorporated into the model for topic discovering. Finally, the generalized EM algorithm is used to estimate the parameters. As our model considers both the local image structure and local word consistency simultaneously when estimating the probabilistic topic distributions, the image representations can have more powerful description ability in the learned latent semantic space. Extensive experiments on the publicly available databases demonstrate the effectiveness of our approach.