Research Article Image Classification Using PSO-SVM and an RGB-D Sensor Carlos López-Franco, Luis Villavicencio, Nancy Arana-Daniel, and Alma Y. Alanis Computer Science Department, CUCEI, University of Guadalajara, 44430 Guadalajara, JAL, Mexico Correspondence should be addressed to Carlos L´ opez-Franco; clzfranco@gmail.com Received 10 February 2014; Revised 29 May 2014; Accepted 13 June 2014; Published 10 July Academic Editor: Francesco Ubertini Copyright © 2014 Carlos L´ opez-Franco et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Image classifcation 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. Te 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 classifcation 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. 1. Introduction Over the past years, there has been an increasing interest in object recognition. Object recognition can be divided in two major tasks: object localization and image classifcation. Object localization detects instances of a given category in the image. Image classifcation can be defned as the task of defning labels to an image, depending on the presence of an object. In this paper we propose an image classifcation system based on invariant moment descriptor that includes depth information. Te 3D data allows producing small and robust descriptors that will improve the image classifcation. Tese descriptors are constructed using object models with rich information of the distribution of points. Te model genera- tion stage requires that best points are selected; therefore this stage can be defned as an optimization problem. Mathematical optimization is the selection of the best element with regard to some criteria. In the simplest case, it is consisted of maximizing or minimizing a ftness function [1]. Metaheuristic designates a computational method that opti- mizes a problem by iteratively trying to improve a candidate solution [2]. Many metaheuristics implement nature-inspired stochastic optimization. One of these algorithms is particle swarm optimization (PSO) developed by [3] and inspired by social behavior of bird focking or fsh schooling. It has been applied in many felds such as tremor analysis for biomedical engineering, trajectory planning [4], electric power [5], and image processing [6]. Optimization algorithms are ofen used in computer vision tasks such as image classifcation, which fnds a relation between an input image and a set of previously known models [7]. Te sensor used in this work is a Kinect [8] which is an RGB-D sensor providing synchronized color and depth images. Tis sensor is widely used by the computer vision community due to its capabilities. In this work we analyze the inclusion of 3D information (provided by an RGB-D sensor) and the use of the PSO algorithm to create robust object models. Using this approach we can construct small and robust descriptors that improve image classifcation. Te rest of the paper is organized as follows. Te next section will present the proposed image classifcation system. In Section 4 we present the mesh optimization process, with a brief introduction to the PSO algorithm. In Section 5 the invariant moment descriptor and classifcation are explained. In Section 6 we show the results of the proposed approach. Finally, in Section 8 we give the conclusions. Hindawi Publishing Corporation Mathematical Problems in Engineering Volume 2014, Article ID 695910, 17 pages http://dx.doi.org/10.1155/2014/695910