## 2 State of the Art  
Since the beginning of the Internet, the need to search and retrieve information has increased. Initially, the information available on Internet was mostly in the form of text [17], but with the technological progress, the information circulating on the Internet has advanced to multimedia data, i.e., text, voice, image, video, and graphics, which require search and retrieval. For each media, there is a need for search and retrieval. In this  
Figure 1: Proposed general methodology.  
research work, search and retrieval of images of natural scenes has been carried out using Wiener-Granger causality.  
Nowadays, people need to search for complete scenes or some elements within the scenes, all this within a context of large and complex image bases. Thus, many important search and retrieval methodologies and algorithms have been presented [18]. Since the existing algorithms have been proven limited in performance, the problem of image search and retrieval is still open for researchers.  
There are different methods of content-based image retrieval, globally, we can classify them into two (See Figure 2):  
* _Conventional CBIR methods:_ These are the methods based on conventional machine learning algorithms, and based on global and local attributes, or the combination of both [7; 12; 17; 18; 19; 20; 21; 22; 23].
* _CNN-based CBIR methods:_ In the recent years, CBIR methods based on neural networks have increased, particularly encoders and convolutional neural networks (CNNs), originating in the use of local and global attributes [24; 25; 26; 27; 28; 29; 30].  
The classification of CBIR system is shown in Figure 2. Table 1 shows a few CBIR methodologies application fields i.e., medical, biology, academic, design, video/images, Covid-19, among others.  
[PERSON] et al. [31] presented a comparative applied CBIR systems review: AKIWI, FIRE, TINEYE, FRAUNHOFER IOSB, VIRAL, LIRE, YANDEX, PASTEC, and MIFILE. The authors present each of the previous applications, specifying the kind of methodology, image, and descriptor used (local, global, neural).  
This research work adopts conventional methodologies using attributes of the Wiener-Granger Causality theory, framed in a self-organizing system of natural settings. The proposed methodology comprises of a stage of feature extraction at random points within the image, after which these features are organized in time-series form, subsequently, the estimation of Wiener-Granger causality is carried out. Once the causal relationships are obtained, a clustering algorithm \" k-means \" is applied to achieve the self-organization of 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. Our methodology is validated on three public image databases. The advantage of the proposed system is that it is able to search for a particular element in a scenery image i.e., clouds, forest, water, among others. Thus, the search and recovery of the proposed system maybe by means of a component of a scene.  
Figure 2: Classification of content-based image retrieval methodologies.