Algorithms for clustering data
Algorithms for clustering data
Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
On Recognizing and Positioning Curved 3-D Objects from Image Contours
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object recognition by computer: the role of geometric constraints
Object recognition by computer: the role of geometric constraints
Thinking (vol. 3)
Discriminant Adaptive Nearest Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
Multimedia Tools and Applications
Machine Learning
When Are k-Nearest Neighbor and Back Propagation Accurate for Feasible Sized Sets of Examples?
Proceedings of the EURASIP Workshop 1990 on Neural Networks
Nonlinear manifold learning for visual speech recognition
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Genetic algorithms for object recognition in a complex scene
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol.2)-Volume 2 - Volume 2
Incremental Learning for Vision-Based Navigation
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume IV-Volume 7472 - Volume 7472
Efficient content-based image retrieval using automatic feature selection
ISCV '95 Proceedings of the International Symposium on Computer Vision
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Hierarchical Discriminant Regression
IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multispace KL for Pattern Representation and Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Towards Self-Exploring Discriminating Features
MLDM '01 Proceedings of the Second International Workshop on Machine Learning and Data Mining in Pattern Recognition
Content-Based Similarity Assessment in Multi-segmented Medical Image Data Bases
MLDM '01 Proceedings of the Second International Workshop on Machine Learning and Data Mining in Pattern Recognition
Visualization, Estimation and User-Modeling for Interactive Browsing of Image Libraries
CIVR '02 Proceedings of the International Conference on Image and Video Retrieval
MKL-Tree: A Hierarchical Data Structure for Indexing Multidimensional Data
DEXA '02 Proceedings of the 13th International Conference on Database and Expert Systems Applications
Evaluation of distance metrics for recognition based on non-negative matrix factorization
Pattern Recognition Letters
IEEE Transactions on Knowledge and Data Engineering
Visualization and User-Modeling for Browsing Personal Photo Libraries
International Journal of Computer Vision - Special Issue on Content-Based Image Retrieval
Integrated Sensing and Processing Decision Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
Coevolutionary feature synthesized EM algorithm for image retrieval
Proceedings of the 13th annual ACM international conference on Multimedia
Fast linear discriminant analysis using binary bases
Pattern Recognition Letters
Dynamic training using multistage clustering for face recognition
Pattern Recognition
Signing Exact English (SEE): Modeling and recognition
Pattern Recognition
Adaptive discriminant analysis for microarray-based classification
ACM Transactions on Knowledge Discovery from Data (TKDD)
Signing Exact English (SEE): Modeling and recognition
Pattern Recognition
Feature synthesized EM algorithm for image retrieval
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
2DLDA-based texture recognition in the aspect of objective image quality assessment
Annales UMCS, Informatica
Discriminant isometric mapping for face recognition
ICVS'03 Proceedings of the 3rd international conference on Computer vision systems
Machine Graphics & Vision International Journal
A new recognition method for natural images
WSEAS Transactions on Computers
Entropy-based iterative face classification
BioID'11 Proceedings of the COST 2101 European conference on Biometrics and ID management
Facial images dimensionality reduction and recognition by means of 2DKLT
Machine Graphics & Vision International Journal
Classification of 3-D objects and faces employing view-based clusters
Computers and Electrical Engineering
GIScience'06 Proceedings of the 4th international conference on Geographic Information Science
Subspace-based clustering and retrieval of 3-D objects
Computers and Electrical Engineering
Hi-index | 0.14 |
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 automatic optimal feature derivation 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. The major characteristics of the proposed hierarchical discriminant analysis include: 1) avoiding the limitation of global linear features (hyperplanes as separators) 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.