Proceedings Article10.1109/ICIP.2008.4711892
Speeding up Support Vector Machine (SVM) image classification by a kernel series expansion
T. Habib,Jordi Inglada,Gregoire Mercier,Jocelyn Chanussot +3 more
- 12 Dec 2008
- pp 865-868
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TL;DR: A new decomposition scheme of the SVM decision function is proposed, based on using the Taylor series expansion to approximate the kernel function to provide an approximate decision function that provides a trade-off between the classification accuracy and the processing time.
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Abstract: Due to their flexibility, and capacity to handle high dimensional vectorial data, support vector machines (SVMs) have become the reference for remote sensing imagery classification. However when processing large amounts of data the SVM classification could be a time consuming process. In this paper a new decomposition scheme of the SVM decision function is proposed. The decomposition is based on using the Taylor series expansion to approximate the kernel function. Then, using the results of the optimization problem of the SVM after the learning phase, this expansion is used to obtain an approximate decision function that provides a trade-off between the classification accuracy and the processing time. This speeds-up the SVM classification if limited processing time is available and favors accuracy if sufficient processing time is available.
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Citations
Spectral–Spatial Classification of Hyperspectral Imagery Based on Partitional Clustering Techniques
TL;DR: A new spectral-spatial classification scheme for hyperspectral images is proposed that improves the classification accuracies and provides classification maps with more homogeneous regions, when compared to pixel wise classification.
An approximation of the Gaussian RBF kernel for efficient classification with SVMs
Matthias Ring,Bjoern M. Eskofier +1 more
TL;DR: A finite-dimensional approximative feature map is derived, based on an orthonormal basis of the kernels RKHS, to enable the reformulation of Gaussian RBF SVMs to linear SVMs and it is shown that the error of this approximatives feature map decreases with factorial growth if the approximation quality is linearly increased.
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•Dissertation
Classification of hyperspectral data using spectral-spatial approaches
Yuliya Tarabalka
- 14 Jun 2010
TL;DR: This thesis proposes and develops novel spectral-spatial methods and algorithms for accurate classification of hyperspectral data and explores possibilities of high-performance parallel computing on commodity processors for reducing computational loads.
25
A Semi-Supervised Image Classification Model Based on Improved Ensemble Projection Algorithm
TL;DR: First, a deformable part-based model is adopted to capture a stable global structure and salient objects, and then, a better decision boundary is found by the classification algorithm-based on an improved ensemble projection (IEP).
17
Reduction of memory footprint and computation time for embedded Support Vector Machine (SVM) by kernel expansion and consolidation
Nikhil Bajaj,George T.-C. Chiu,Jan P. Allebach +2 more
- 20 Nov 2014
TL;DR: In this paper, decomposition methods for SVM classification functions are developed and discussed, using polynomial approximation methods, and in three demonstrated example systems, the classifier is made two orders of magnitude faster, with a memory requirement that is two order of magnitude smaller.
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