###### Abstract  
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.  
image classification; image retrieval; Wiener-Granger causality +
Footnote †: journal: Medical  
In this work, a natural scene retrieval system is developed by applying the Wiener-Granger Causality theory (WGC) [11] as a tool to analyze images through self-organized information. The causal relationships between the local textures contained in an image were identified, which helps in characterizing a descriptive pattern of a set of natural scenes within an image data set.  
The main stages (shown in Figure 1) involved in the developed system are as follows:  
Image reading: The images in the dataset are read, and then a color space change is applied from Red-Green-Blue (RGB) space to Hue-Saturation-Intensity (HSI) space.  
Feature Extraction: CBIR statistical feature extraction is randomly generated based on 300 image points.  
Time series conformation: Texture features are organized as a time series for each image.  
Causality analysis: WGC analysis is applied to calculate the causal relationship matrix between different textures.  
Classification application: The \(k-NN\) k-next neighbors classification algorithm is used to find the features closest to or similar to the searched one.  
The work proposes a causality analysis of the natural scene classes based on an auto-generated texture dictionary and the WGC analysis of the CBIR features [12; 13] to provide the characterization of the data set. It is also possible to search for a type of constitutive element of the scenes, i.e., water, clouds, forests, etc. The proposed system recovers all the scenes that contain a particular element, even when they are classified into different natural scenes.  
The proposed methodology was tested on three natural scenes databases: Vogel and Shiele (V_S) [14], Oliva and Torralba (O_T) [15], and Shullani (Sh) [16]. The proposed method in this research work can be implemented in an autonomous natural scene recognition system mounted on a car or drone with 100% certainty.  
The rest of the paper is organized as follows. In Section 3, the theoretical support of the WGC model to be applied is presented. In Section 4, the methodology is presented. The results are presented in Section 5. Finally, the conclusions and future works are presented in Section 6.