Hierarchical discriminant analysis for image retrieval

A self-organizing framework for object recognition is described. We describe a hierarchical database structure for image retrieval. The self-organizing hierarchical optimal subspace learning and inference framework (SHOSLIF) system uses the theories of optimal linear projection for optimal feature d...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence 1999-05, Vol.21 (5), p.386-401
Main Authors: Swets, D.L., Juyang Weng
Format: Article
Language:English
Subjects:
Boundaries
Character generation
Character recognition
Image analysis
Image databases
Image retrieval
Information retrieval
Learning
Object recognition
Optimization
Partitioning
Principal component analysis
Retrieval
Spatial databases
Testing
Trees
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:A self-organizing framework for object recognition is described. We describe a hierarchical database structure for image retrieval. The self-organizing hierarchical optimal subspace learning and inference framework (SHOSLIF) system uses the theories of optimal linear projection for optimal feature derivation and a hierarchical structure to achieve logarithmic retrieval complexity. A space-tessellation tree is generated using the most expressive features (MEF) and most discriminating features (MDF) at each level of the tree. The major characteristics of the analysis include: (1) avoiding the limitation of global linear features by deriving a recursively better-fitted set of features for each of the recursively subdivided sets of training samples; (2) generating a smaller tree whose cell boundaries separate the samples along the class boundaries better than the principal component analysis, thereby giving a better generalization capability (i.e., better recognition rate in a disjoint test); (3) accelerating the retrieval using a tree structure for data pruning, utilizing a different set of discriminant features 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.
ISSN:0162-8828
1939-3539
DOI:10.1109/34.765652