A Fuzzy Approach on Image Complexity Measure
Enfoque Difuso Para la Medición de la Complejidad de Imágenes
Mario Ignacio Chacón Murguía, Alma Delia Corral Sáenz and Rafael Sandoval Rodríguez
Chihuahua Institute of Technology, DSP & Vision Laboratory
Av. Tecnológico 2909 ]]>
Chihuahua, Chih., México C.P. 31310 Tel.4–13–74–74 Ext 112 y 114
mchacon@itchihuhahua.edu.mx
Article received on March 08, 2007; accepted on April 26, 2007
Abstract
This paper describes a novel fuzzy based approach to determine the complexity of an image which is independent of a human perception criterion. The proposed method determines the complexity of an image based on the analysis of its edge level percentages. First, the method determines the complexity class of an image from among three classes, Little Complex, More or Less Complex, and Very Complex using centroids obtained from a fuzzy clustering process. Second, the membership value for that class is computed by a set of interval mapping functions. The method is very robust and consistent since it does not incorporate any a priori human evaluation of complexity. Results of the method show a correlation with human complexity values obtained in an independent evaluation test; however, the values obtained with our method are consistent and not subject to the viewer's subjectivity. The paper also shows promising results in applying the method to an application of determining the edges of images when compared with a crisp image complexity method.
Keywords: Image complexity, Fuzzy logic, Image processing.
Resumen
]]> Este artículo describe un nuevo enfoque basado en lógica difusa para determinar la complejidad de una imagen, el cual es independiente del criterio de la percepción humana. El método propuesto determinar la complejidad de una imagen mediante el análisis de los porcentajes de niveles de bordes de la imagen. El método determina primero la clase de complejidad de la imagen entre tres clases, Poco Compleja, Más o Menos Compleja y Muy Compleja usando centros de grupos obtenidos mediante un proceso de agrupamiento difuso. Después, el grado de pertenencia a esa clase es calculado mediante un conjunto de funciones de mapeo de intervalos. El método es muy robusto y consistente ya que no incorpora ninguna evaluación humana a priori de la complejidad. Los resultados del método muestran una correlación con los valores de complejidad asignados por observadores humanaos en una prueba de evaluación independiente, sin embargo, los valores obtenidos con el método propuesto son consistentes y no sujetos a la subjetividad del visor. El artículo presenta también resultados promisorios in la aplicación del método para la determinación de bordes de imágenes cuando se compara con un método de complejidad rígido.Palabras clave: Complejidad de Imagen, Lógica Difusa, Procesamiento de Imágenes.
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Acknowledgment
The authors appreciate the support of COSNET, and SEP–DGEST for the support of this research under grant 445.05–P.
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