Título

Multi-class particle swarm model selection for automatic image annotation

Autor

Hugo Jair Escalante Balderas

Manuel Montes y Gómez

Luis Enrique Sucar Succar

Nivel de Acceso

Acceso Abierto

Resumen o descripción

This article describes the application of particle swarm model selection (PSMS) to the problem of automatic image annotation (AIA). PSMS can be considered a black-box tool for the selection of effective classifiers in binary classification problems. We face the AIA problem as one of multi-class classification, considering a one-vs-all (OVA) strategy. OVA makes a multi-class problem into a series of binary classification problems, each of which deals with whether a region belongs to a particular class or not. We use PSMS to select the models that compose the OVA classifier and propose a new technique for making multi-class decisions from the selected classifiers. This way, effective classifiers can be obtained in acceptable times; specific methods for preprocessing, feature selection and classification are selected for each class; and, most importantly, very good annotation performance can be obtained. We present experimental results in six data sets that give evidence of the validity of our approach; to the best of our knowledge the results reported herein are the best obtained so far in the data sets we consider. It is important to emphasize that despite the application domain we consider is AIA, nothing restricts us of applying the methods described in this article to any other multi-class classification problem.

Editor

Elsevier Ltd.

Fecha de publicación

2012

Tipo de publicación

Artículo

Versión de la publicación

Versión aceptada

Formato

application/pdf

Idioma

Inglés

Audiencia

Estudiantes

Investigadores

Público en general

Sugerencia de citación

Escalante-Balderas, H.J., et al., (2012). Multi-class particle swarm model selection for automatic image annotation, Expert Systems with Applications, (39): 11011–11021

Repositorio Orígen

Repositorio Institucional del INAOE

Descargas

210

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