Abstract
Image recognition and classification have been widely used for research in computer vision systems. This paper aims to implement a new strategy called Wiener-Granger Causality theory for classifying natural scenery images. This strategy is based on self-content images extracted using a Content-Based Image Retrieval (CBIR) methodology (to obtain different texture features); later, a Genetic Algorithm (GA) is implemented to select the most relevant natural elements from the images which share similar causality patterns. The proposed method is comprised of a sequential feature extraction stage, a time series conformation task, a causality estimation phase, causality feature selection throughout the GA implementation (using the classification process into the fitness function). A classification stage was implemented and 700 images of natural scenery were used for validating the results. Tested in the distribution system implementation, the technical efficiency of the developed system is 100% and 96% for resubstitution and cross-validation methodologies, respectively. This proposal could help with recognizing natural scenarios in the navigation of an autonomous car or possibly a drone, being an important element in the safety of autonomous vehicles navigation.
Highlights
One of the challenges researchers face today is developing an artificial authentication system that has acquisition and processing capabilities similar to those possessed by humans [1]
We describe the methodology developed for the Wiener-Granger Causality (WGC) technique with a Genetic Algorithm (GA)
Looking for the analysis of the Γ matrices generated by the WGC to find the significant causality relationships for one scenery, we propose each matrix to be treated with a GA implementation
Summary
One of the challenges researchers face today is developing an artificial authentication system that has acquisition and processing capabilities similar to those possessed by humans [1]. Artificial vision is defined as the capacity of a machine to see the world that surrounds it in a 3-Dimensional form starting from a group of 2-Dimensional images [2]. A computer vision system is composed of different stages that work together for solving a particular problem [3]. Automatic image recognition is among the problems that might be solved using computer vision systems. Researchers are eager to develop these systems and different techniques have been implemented for their improvement, such as machine learning, pattern recognition and evolutionary algorithms
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