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
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Object Categorization by Learned Universal Visual Dictionary
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Scene Classification Using a Hybrid Generative/Discriminative Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Continuous visual vocabulary modelsfor pLSA-based scene recognition
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Comparing Local Feature Descriptors in pLSA-Based Image Models
Proceedings of the 30th DAGM symposium on Pattern Recognition
Distance Metric Learning for Large Margin Nearest Neighbor Classification
The Journal of Machine Learning Research
Image categorization via robust pLSA
Pattern Recognition Letters
Large margin nearest neighbor classifiers
IEEE Transactions on Neural Networks
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Probabilistic topic models have been applied to image classification and permit to obtain good results. However, these methods assumed that all topics have an equal contribution to classification. We propose a weight learning approach for identifying the discriminative power of each topic. The weights are employed to define the similarity distance for the subsequent classifier, e.g. KNN or SVM. Experiments show that the proposed method performs effectively for image classification.