Hierarchical Discriminant Analysis for
نویسنده
چکیده
|A self-organizing framework for object recognition is described. We describe a hierarchical database structure for image retrieval. The SHOSLIF (Self-Organizing Hierarchical Optimal Subspace Learning and Inference Framework) system uses the theories of optimal linear projection for automatic optimal feature selection and a hierarchical structure to achieve a logarithmic retrieval complexity. A Space-Tessellation Tree is automatically generated using the Most Expressive Features (MEFs) and the Most Discriminating Features (MDFs) at each level of the tree. We allow for perturbations in the size and position of objects in the images through learning. We demonstrate the technique on a large image database of widely varying real-world objects taken in natural settings, and show the applicability of the approach for variability in position, size, and 3D orientation. This paper concentrates on the hierarchical partitioning of the feature spaces.
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