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Image classification using correlation tensor analysis.

Yun Fu¹, Thomas S Huang

¹Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA. yunfu2@ifp.uiuc.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|February 14, 2008

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Correlation Tensor Analysis (CTA) improves image classification by using a correlation-based similarity metric. This novel approach enhances discriminant subspace learning for better performance in tasks like face recognition.

Area of Science:

Computer Science
Machine Learning
Image Processing

Background:

Image classification requires algorithms that consider data structure, similarity metrics, discriminant subspaces, and classifiers.
Existing methods often use Fisher criterion, graph embedding, and tensorization for subspace learning.

Purpose of the Study:

To introduce a novel discriminant subspace learning algorithm, Correlation Tensor Analysis (CTA).
To demonstrate that a correlation-based similarity metric can improve classification performance beyond existing methods.

Main Methods:

Developed Correlation Tensor Analysis (CTA), a supervised multilinear discriminant subspace learning algorithm.
Incorporated graph-embedded correlational mapping and Fisher-type discriminant analysis.
Utilized a correlation metric for estimating intrinsic angles and distances in locally isometric embedding.

Related Experiment Videos

Main Results:

CTA effectively handles data where Euclidean metrics fail to capture intrinsic similarities.
The algorithm learns interrelated subspaces for low-dimensional data representation.
Extensive comparisons show CTA's superiority over popular subspace learning methods in face recognition.

Conclusions:

Correlation-based similarity metrics enhance discriminant subspace learning for image classification.
CTA offers a robust and effective approach for high-dimensional image data analysis.
The method demonstrates significant improvements in classification performance, particularly in face recognition.