Abstract
Texture is an important visual attribute used to describe images. There are many methods available for texture analysis. However, most of these methods are object to variant rotation and changing scale of the images. Hence, this study presents a novel approach for texture analysis. The approach applies the Particle Swarm Optimization Algorithm in learning the texture filters for texture classifications. In this approach, the texture filter is regarded as the particle; the population of particle is iteratively evaluated according to a statistical performance index corresponding to object classification ability and evolves into the optimal filter using the evolution principles of Particle Swarm Optimization Algorithm. The method has been validated on aerial images and results indicate that proposed method is feasible for texture analysis.
Highlights
Image texture, defined as a function of the spatial variation in pixel intensities, is useful in a variety of applications and one of the most important visual attributes for image analysis and machine vision, an immediate application of image texture is the recognition and description of image regions using texture properties (Rafael and Richard, 2010)
In order to overcome these drawbacks, this study presents a novel methodology to learn optimal tuned filter by the medium of employing Particle Swarm Optimization algorithm (PSO) (Kennedy and Eberhart, 1995)
This study has presented a novel approach of texture feature extraction for classifying and detecting texture objects from aerial imagery using the learning techniques based on PSO
Summary
Image texture, defined as a function of the spatial variation in pixel intensities, is useful in a variety of applications and one of the most important visual attributes for image analysis and machine vision, an immediate application of image texture is the recognition and description of image regions using texture properties (Rafael and Richard, 2010). In order to solve the problem, You and Cohen (1993) presented some new methods to extract a robust texture feature by guided search procedure. The principle that texture statistic utilized to represent the texture feature is the normalized “texture energy” derived from Law’s approach, i.e. the variance or mean of pixel gray scale within a 9×9 window, which is generated by convolution with the optimal texture filter obtained from task-aimed training Their experimental results on texture images indicated that optimal texture filters could be employed to extract robust texture features which were invariant to orientation and scale changes of the texture. Fitness function: PSO needs to define a function which measures the overall classification quality of the tuned filter and guide the algorithm to search the optimal solution.
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