Recovery of Natural Scenery Image by Content Using Wiener-Granger Causality: A Self-Organizing Methodology

• Published in: Applied sciences Vol. 11; no. 19; p. 8795
• Main Authors: Benavides-Alvarez, Cesar, Aviles-Cruz, Carlos, Rodriguez-Martinez, Eduardo, Ferreyra-Ramírez, Andrés, Zúñiga-López, Arturo
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2021
• Subjects: Algorithms; Artificial intelligence; Causality; Classification; Digital imaging; image classificaction; Image classification; Image retrieval; Internet; Machine learning; Methods; Neural networks; Scientific papers; Time series; Wiener-Granger causality
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app11198795

One of the most important applications of data science and data mining is is organizing, classifying, and retrieving digital images on Internet. The current focus of the researchers is to develop methods for the content based exploration of natural scenery images. In this research paper, a self-organizing method of natural scenes images using Wiener-Granger Causality theory is proposed. It is achieved by carrying out Wiener-Granger causality for organizing the features in the time series form and introducing a characteristics extraction stage at random points within the image. Once the causal relationships are obtained, the k-means algorithm is applied to achieve the self-organizing of these attributes. Regarding classification, the k−NN distance classification algorithm is used to find the most similar images that share the causal relationships between the elements of the scenes. The proposed methodology is validated on three public image databases, obtaining 100% recovery results.