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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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Published on: June 26, 2013

A method for compact image representation using sparse matrix and tensor projections onto exemplar orthonormal bases.

Karthik S Gurumoorthy¹, Ajit Rajwade, Arunava Banerjee

¹Department of Computer and InformationScience and Engineering, University of Florida, Gainesville, FL 32611, USA. ksg@cise.ufl.edu

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

|October 23, 2009

Summary

This study introduces a novel matrix-based method for compact image representation, outperforming JPEG in compression tests. The technique efficiently encodes image patches using learned bases, proving effective for diverse datasets and robust to noise.

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Area of Science:

Computer Vision
Image Processing
Machine Learning

Background:

Conventional image representation often uses vectorial methods.
Existing compression techniques like JPEG have limitations in representing complex image data.

Purpose of the Study:

To develop a new, compact representation method for large image datasets.
To improve image compression efficiency and performance.

Main Methods:

Treating image patches as matrices instead of vectors.
Encoding patches via sparse projections onto learned orthonormal bases.
Extending the method to higher-order tensors for color image compression.

Main Results:

Achieved low-error, highly compact image/patch representation.
Demonstrated favorable comparison with JPEG on face and natural image databases.
Showcased tunability for patches of varying complexity and robustness to image noise.

Conclusions:

The proposed matrix-based method offers significant theoretical merits and practical advantages for image compression.
The technique is adaptable and performs competitively with established methods like JPEG.