Wiener–Granger Causality Theory Supported by a Genetic Algorithm to Characterize Natural Scenery

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.Electronics 2019, 8, 726 2 of 25 human hands). Currently, thousands of images are generated via different kinds of sources on a daily basis and the constant increase of the Internet has influenced human life.More than half of the information on the Internet is images, 85% of which were taken with mobile devices with a final estimation of 5 trillion images reported so far [4].In order to use this information efficiently, an image recovery system based on Content-Based Image Retrieval (CBIR) is necessary. It will help users to find relevant images based on their self-content features or those which are "seen" to e related to them, from our visual perception, even when there is no previous knowledge of the database, such as manual labeling of the images.Our previous work successfully applied the CBIR technique to the face recognition problem [5,6]. The multiple textures, objects in unknown positions and their different compositions in natural scenery images challenge the proposals that combine different techniques for obtaining a better performance of natural scenery image classification. In this work, we use CBIR feature extraction as an input of a texture causality engine to characterize 5 scenery types, manually defining a base dictionary conformed by 4 textures. In future work, conforming this dictionary is planned to be dynamical, considering more base textures and scenery types to improve classification performance.In this work, an image retrieval system of natural scenery images is developed by applying the Wiener-Granger Causality (WGC) theory [7] as a tool for analyzing images throughout self-content information. The causal relationships between local textures contained in an image were identified, leading to characterization of a descriptive pattern of a s...

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