On Recognizing and Positioning Curved 3-D Objects from Image Contours
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
OVID: Design and Implementation of a Video-Object Database System
IEEE Transactions on Knowledge and Data Engineering
Journal of Cognitive Neuroscience
Hierarchical Discriminant Regression for Incremental and Real-Time Image Classification
IDEAL '00 Proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents
Dynamic Grouping Strategies Based on a Conceptual Graph for Cooperative Learning
IEEE Transactions on Knowledge and Data Engineering
International Journal of Distance Education Technologies
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Fisher's discriminant analysis is very powerful for classification but it does not perform well when the number of classes is large but the number of samples in each class is small. We propose to resolve this problem by dynamically grouping classes at different levels in a tree. We recast the problem of classification as a regression problem so that the classification (class labels as output) and regression (numerical values as output) are unified. The proposed HDR tree automatically forms clusters in the input space guided by the desired out-put, which produces discriminant spaces. These discriminant spaces are organized in a coarse-to-fine structure by a tree. A unified size-dependent negative-log-likelihood is proposed to automatically handle both under-sample situations (where the number of samples of each cluster is smaller than the dimensionality of the discriminant space) and the over-sample situations where the HDR tree can reach near-optimal performance. For fast computation, the HDR tree has a logarithmic retrieval time complexity. The proposed HDR tree has been tested with synthetic data, face image databases, and publicly available data sets that use manually selected features.