Two soft relatives of learning vector quantization
Neural Networks
Soft vector quantization and the EM algorithm
Neural Networks
Making large-scale support vector machine learning practical
Advances in kernel methods
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons
International Journal of Computer Vision
Indoor-Outdoor Image Classification
CAIVD '98 Proceedings of the 1998 International Workshop on Content-Based Access of Image and Video Databases (CAIVD '98)
The Journal of Machine Learning Research
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Image Categorization by Learning and Reasoning with Regions
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
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Towards optimal bag-of-features for object categorization and semantic video retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Continuous visual vocabulary modelsfor pLSA-based scene recognition
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Kernel Codebooks for Scene Categorization
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Hyperfeatures – multilevel local coding for visual recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
The 2005 PASCAL visual object classes challenge
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
Image classification for content-based indexing
IEEE Transactions on Image Processing
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In the last few years, bag of local features has become a popular approach for image categorization and achieved impressive performances. This approach assigns each local feature to a visual word from the visual vocabulary and represent image as a histogram of visual words. And some extensions can further improve bag of local feature approach. For example, probabilistic topic models can be adopted to capture co-occurrences between visual words in the image collection; soft vector quantization can be applied to reduce the information loss in quantization. In despite of its good performance, bag of local features approach does not correspond to human visual perception process, and studies on human perception suggest a region based approach. In this paper, we investigate applying pLSA, a probabilistic topic model, to region-based image classification, and propose two soft vector quantization methods to tackle the small sample problem in visual vocabulary construction. Our experiment indicates that applying pLSA to region-based image categorization with soft vector quantization is an effective approach.