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Image Classification Using PSO-SVM and an RGB-D Sensor
Joint Authors
López-Franco, Carlos
Arana-Daniel, Nancy
Villavicencio, Luis
Alanis, Alma Y.
Source
Mathematical Problems in Engineering
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-17, 17 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-07-10
Country of Publication
Egypt
No. of Pages
17
Main Subjects
Abstract EN
Image classification is a process that depends on the descriptor used to represent an object.
To create such descriptors we use object models with rich information of the distribution of points.
The object model stage is improved with an optimization process by spreading the point that conforms the mesh.
In this paper, particle swarm optimization (PSO) is used to improve the model generation, while for the classification problem a support vector machine (SVM) is used.
In order to measure the performance of the proposed method a group of objects from a public RGB-D object data set has been used.
Experimental results show that our approach improves the distribution on the feature space of the model, which allows to reduce the number of support vectors obtained in the training process.
American Psychological Association (APA)
López-Franco, Carlos& Villavicencio, Luis& Arana-Daniel, Nancy& Alanis, Alma Y.. 2014. Image Classification Using PSO-SVM and an RGB-D Sensor. Mathematical Problems in Engineering،Vol. 2014, no. 2014, pp.1-17.
https://search.emarefa.net/detail/BIM-491315
Modern Language Association (MLA)
López-Franco, Carlos…[et al.]. Image Classification Using PSO-SVM and an RGB-D Sensor. Mathematical Problems in Engineering No. 2014 (2014), pp.1-17.
https://search.emarefa.net/detail/BIM-491315
American Medical Association (AMA)
López-Franco, Carlos& Villavicencio, Luis& Arana-Daniel, Nancy& Alanis, Alma Y.. Image Classification Using PSO-SVM and an RGB-D Sensor. Mathematical Problems in Engineering. 2014. Vol. 2014, no. 2014, pp.1-17.
https://search.emarefa.net/detail/BIM-491315
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-491315