NDLI: Content-based image classification using PSO-SVM in fuzzy topological space

Content Provider	IEEE Xplore Digital Library
Author	Saxena, U. Goyal, A.
Copyright Year	2013
Description	Author affiliation: Inf. Technol., Samrat Ashok Technol. Inst., Vidisha, India (Saxena, U.; Goyal, A.)
Abstract	In this paper, the concept of particle swarm optimization (PSO) with support vector machine (SVM) in fuzzy topological space is proposed for classifying a given image. SVMs have their roots in statistical learning theory and have gained prominence because they are more accurate and reliable even if a limited number of training samples are used. However, the task of parameter selection in SVMs is quite tedious, as they require the selection of the kernel function parameter. In order to overcome this problem, the support vector machine is trained using the particle swarm optimization technique. In the proposed approach, this classification scheme is applied to the content selected for the classification. Moreover, in order to deal with the mixed pixel problem the input image class in the spectral space is decomposed into three parts: the interior, the boundary and the exterior in fuzzy topological space (fts). This decomposition is done using a threshold value based on the optimal intercorrelation coefficient. The interior-class pixels are classified using the PSO-SVM. The boundary-class pixels that are the fuzzy ones are reclassified on the basis of the connectivity theory of the fuzzy topology. The results of the experiment conducted on the remotely sensed image indicate that the proposed approach is more accurate than the existing fuzzy topology integrated support vector machine (FTSVM). Thus, it provides a better classification scheme.
Starting Page	72
Ending Page	78
File Size	1344183
Page Count	7
File Format	PDF
ISBN	9781479915699
e-ISBN	9781479915729
DOI	10.1109/ICCCT.2013.6749606
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-20
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training Fuzzy Topology Topology Classification algorithms Particle swarm optimization Remote sensing Image classification Threshold Support Vector Machines(SVMs) Particle Swarm Optimization(PSO)
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Model parameters selection for SVM classification using Particle Swarm Optimization

Remote Sensing Satellite Images Classification Using Support Vector Machine and Particle Swarm Optimization

A new watershed segmentation (NWS) and particle swarm optimization (PSO-SVM) techniques in remote sensing image retrieval

Semisupervised Hyperspectral Image Classification with SVM and PSO

Classification Based on Particle Swarm Optimization for Least Square Support Vector Machines Training

Optimization of SVM MultiClass by Particle Swarm (PSO-SVM)

Gene selection in cancer classification using PSO/SVM and GA/SVM hybrid algorithms

PSO Parameters Optimization Based Support Vector Machines for Hyperspectral Classification

Multispectral remote sensing image classification based on PSO-BP considering texture

Content-based image classification using PSO-SVM in fuzzy topological space

Similar Documents

Model parameters selection for SVM classification using Particle Swarm Optimization

Remote Sensing Satellite Images Classification Using Support Vector Machine and Particle Swarm Optimization

A new watershed segmentation (NWS) and particle swarm optimization (PSO-SVM) techniques in remote sensing image retrieval

Semisupervised Hyperspectral Image Classification with SVM and PSO

Classification Based on Particle Swarm Optimization for Least Square Support Vector Machines Training

Optimization of SVM MultiClass by Particle Swarm (PSO-SVM)

Gene selection in cancer classification using PSO/SVM and GA/SVM hybrid algorithms

PSO Parameters Optimization Based Support Vector Machines for Hyperspectral Classification

Multispectral remote sensing image classification based on PSO-BP considering texture

Content-based image classification using PSO-SVM in fuzzy topological space