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A Fast Image Retrieval Algorithm with Automatically Extracted Discriminant Features (1999)
| Content Provider | CiteSeerX |
|---|---|
| Author | Weng, John J. John, Wey-Shiuan Hwang Qian, Jianzhong |
| Abstract | 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 output, 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, t... |
| File Format | |
| Publisher Date | 1999-01-01 |
| Access Restriction | Open |
| Subject Keyword | Under-sample Situation Class Label Input Space Different Level Regression Problem Near-optimal Performance Fast Image Retrieval Algorithm Fast Computation Discriminant Analysis Unified Size-dependent Negative-log-likelihood Over-sample Situation Form Cluster Numerical Value Automatically Extracted Discriminant Feature Coarse-to-fine Structure Discriminant Space Desired Output Hdr Tree |
| Content Type | Text |
