Dimensionality Reduction of Features using Multi Resolution Representation of Decomposed Images

Avi Bleiweiss

2014

Abstract

A common objective in multi class, image analysis is to reduce the dimensionality of input data, and capture the most discriminant features in the projected space. In this work, we investigate a system that first finds clusters of similar points in feature space, using a nearest neighbor, graph based decomposition algorithm. This process transforms the original image data on to a subspace of identical dimensionality, but at a much flatter, color gamut. The intermediate representation of the segmented image, follows an effective, local descriptor operator that yields a marked compact feature vector, compared to the one obtained from a descriptor, immediately succeeding the native image. For evaluation, we study a generalized, multi resolution representation of decomposed images, parameterized by a broad range of a decreasing number of clusters. We conduct experiments on both non and correlated image sets, expressed in raw feature vectors of one million elements each, and demonstrate robust accuracy in applying our features to a linear SVM classifier. Compared to state-of-the-art systems of identical goals, our method shows increased dimensionality reduction, at a consistent feature matching performance.

Paper Citation

in Harvard Style

Bleiweiss A. (2014). Dimensionality Reduction of Features using Multi Resolution Representation of Decomposed Images . In Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-018-5, pages 316-324. DOI: 10.5220/0004917403160324

in Bibtex Style

@conference{icpram14,
author={Avi Bleiweiss},
title={Dimensionality Reduction of Features using Multi Resolution Representation of Decomposed Images},
booktitle={Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2014},
pages={316-324},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004917403160324},
isbn={978-989-758-018-5},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Dimensionality Reduction of Features using Multi Resolution Representation of Decomposed Images
SN - 978-989-758-018-5
AU - Bleiweiss A.
PY - 2014
SP - 316
EP - 324
DO - 10.5220/0004917403160324