Algorithms for clustering data
Algorithms for clustering data
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
The nature of statistical learning theory
The nature of statistical learning theory
Support Vector Machines for 3D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical Discriminant Analysis for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Multispace KL for Pattern Representation and Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminant Analysis of Principal Components for Face Recognition
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
A Hidden Markov Model approach for appearance-based 3D object recognition
Pattern Recognition Letters
Subclass Discriminant Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
2D face recognition based on supervised subspace learning from 3D models
Pattern Recognition
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
A multiobjective simultaneous learning framework for clustering and classification
IEEE Transactions on Neural Networks
Computers and Electrical Engineering
Competitive learning and soft competition for vector quantizerdesign
IEEE Transactions on Signal Processing
Learning multiview face subspaces and facial pose estimation using independent component analysis
IEEE Transactions on Image Processing
Principal components null space analysis for image and video classification
IEEE Transactions on Image Processing
3-D Object Recognition Using 2-D Views
IEEE Transactions on Image Processing
Survey of clustering algorithms
IEEE Transactions on Neural Networks
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This paper presents the design of a new clustering algorithm for images having wide range of variations in appearances and shape. The major chore of the clustering process involves in creating the partitions, reassigning the elements of the partitions and identifying the compact cluster obtained. The clusters are created from various low-dimensional spaces of the data set. Hierarchically related eigenspaces are employed to reassign the elements of the cluster. The clusters obtained from the proposed clustering scheme are used to form the learning set of the classification module. The quality of clusters generated is evaluated from the classification results. Comparisons on the clustering performance have been made with the well-known K-means and nearest neighbor-based clustering techniques. Excellent performance of the proposed clustering scheme is proved from the results reported. The benchmark datasets for objects and faces having images with large pose variations have been used to illustrate the efficiency and effectiveness of the proposed scheme.