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A NOVEL GENERALIZED ASSIGNMENT FRAMEWORK FOR THE CLASSIFICATION OF HYPERSPECTRAL IMAGE

Abstract: 

Recently, sparse representation based classification has been widely used in pattern recognition. Most of existing methods exploit the recovered representation coefficients to reconstruct the inputs, and the classwise reconstruction errors are used to identify the class of the sample based on the subspace assumption. Different from the reconstruction pipeline, an assignment framework is built on the representation coefficients in this paper. More specifically, we treat the representation coefficients as soft assignments of the class labels, and the distribution of the assignments reveals the class of the sample. Under this framework, we can easily generalize it to multi-sample and/or multi-feature scenarios, where multiple assignment instances can be directly fused to stabilize the distribution estimation. As such, the estimated distribution pattern can be used as a new discriminative feature for classification. Experiments on the classification of hyperspectral image demonstrate that the generalized assignment framework can effectively combine neighboring samples and multiple features for collaborative classification, which could achieve significantly better results than several state-of-the-arts.

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Paper Details

Authors:
Ding Ni, Hongbing Ma
Submitted On:
12 March 2016 - 9:09pm
Short Link:
Type:
Poster
Event:
Presenter's Name:
Ding Ni
Document Year:
2016
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Document Files

ICASSP2016_Ding Ni_poster.pdf

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[1] Ding Ni, Hongbing Ma, "A NOVEL GENERALIZED ASSIGNMENT FRAMEWORK FOR THE CLASSIFICATION OF HYPERSPECTRAL IMAGE", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/648. Accessed: Jul. 11, 2020.
@article{648-16,
url = {http://sigport.org/648},
author = {Ding Ni; Hongbing Ma },
publisher = {IEEE SigPort},
title = {A NOVEL GENERALIZED ASSIGNMENT FRAMEWORK FOR THE CLASSIFICATION OF HYPERSPECTRAL IMAGE},
year = {2016} }
TY - EJOUR
T1 - A NOVEL GENERALIZED ASSIGNMENT FRAMEWORK FOR THE CLASSIFICATION OF HYPERSPECTRAL IMAGE
AU - Ding Ni; Hongbing Ma
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/648
ER -
Ding Ni, Hongbing Ma. (2016). A NOVEL GENERALIZED ASSIGNMENT FRAMEWORK FOR THE CLASSIFICATION OF HYPERSPECTRAL IMAGE. IEEE SigPort. http://sigport.org/648
Ding Ni, Hongbing Ma, 2016. A NOVEL GENERALIZED ASSIGNMENT FRAMEWORK FOR THE CLASSIFICATION OF HYPERSPECTRAL IMAGE. Available at: http://sigport.org/648.
Ding Ni, Hongbing Ma. (2016). "A NOVEL GENERALIZED ASSIGNMENT FRAMEWORK FOR THE CLASSIFICATION OF HYPERSPECTRAL IMAGE." Web.
1. Ding Ni, Hongbing Ma. A NOVEL GENERALIZED ASSIGNMENT FRAMEWORK FOR THE CLASSIFICATION OF HYPERSPECTRAL IMAGE [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/648