NDLI: Fast and accurate image classification with histogram based features and additive kernel SVM

Content Provider	IEEE Xplore Digital Library
Author	Demir, B. Bruzzone, L.
Copyright Year	2015
Description	Author affiliation: Dept. of Inf. Eng. & Comput. Sci., Univ. of Trento, Trento, Italy (Demir, B.; Bruzzone, L.)
Abstract	Kernel-based image classification methods rely on the considered kernel functions that can be chosen with respect to prior information on the adopted features. In remote sensing, histogram features have recently gained an increasing interest due to their capability to address several critical classification problems (e.g., the problem of curse of dimensionality) when appropriate kernels and classifiers are selected. In view of that, in this paper we introduce in remote sensing additive kernels in the context of support vector machine classification (AK-SVM), which are suitable kernels for histogram based feature representations. In particular, we investigate the Histogram Intersection kernel and the chi-square kernel within the AK-SVM. Moreover, we present fast implementations of the AK-SVM to significantly speed up the classification phase of the SVM. Experimental results show the effectiveness of the AK-SVM in terms of classification accuracy and computational time when compared to SVMs with standard kernels.
Starting Page	2350
Ending Page	2353
File Size	228864
Page Count	4
File Format	PDF
e-ISBN	9781479979295
DOI	10.1109/IGARSS.2015.7326280
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-26
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kernel Histograms Support vector machines Accuracy Additives Standards Polynomials remote sensing Additive kernels support vector machines histogram intersection kernel chi-square kernel histogram based features
Content Type	Text
Resource Type	Article

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