**3. Image classification**

*Multimedia Information Retrieval*

specific domain descriptors [7, 8]:

regions, textures and movement.

contour and the [16] region.

deviation, color skewness, variance, median, etc.

Parametric Motion Descriptor (WMD and PMD).

**2.1 General descriptors**

For a long time, high calculation errands caused by calculating complexity and gigantic amount of image during indexing, and retrieving steps have been obstacles for building a CBIR systems [3, 4]. Furthermore, the conventional content-based image retrieval systems have focused on small databases of face images. Therefore, it is important to generalize and train these systems on large-scale databases [5, 6]. Therefore, in this chapter, we will present the basics of CBIR systems for large-scale databases, Big Data, Big Data processing platforms for large scale image retrieval.

The extraction of features from images is the basis of any computer vision system that does recognizing. These characteristics can contain both text (keywords; annotations, etc.), and visual characteristics (color, texture, shapes, faces, etc.). We will focus on techniques for extracting these visual features only. And for that the visual characteristics (descriptors) are classified in two categories general descriptors and

They contain low-level descriptors that give a description of color, shape,

**Color:** Color is one of the most used visual characteristics in facial recognition systems or anything like that. It is relatively robust to the complexities of the background and independently of the size and orientation of the image. The most well-known representation of color is the histogram, which denotes the frequencies of occurrence of the intensities of the three color channels. Many other representations of this characteristic exist: we speak especially of the moments of color. The mathematical basis of this approach is that each color distribution can be characterized by its color moments. Furthermore, most of the information on color is concentrated on lower order moments which are respectively: mean, standard

**Texture:** A wide variety of texture descriptors have been proposed in the literature. These were traditionally divided into statistical, spectral, structural and hybrid [9] approaches. Among the most popular traditional methods are probably those based on histograms, Gabor filters [10], co-occurrence matrices [11] and models (lbp) [12]. These descriptors present various strengths and weaknesses, in particular as regards their invariance with respect to the acquisition conditions.

**Shape:** Over the past two decades, 2D shape descriptors have been actively used

**Movement:** Movement is related to the movement of objects in the sequence and to the movement of the camera. The latter information is provided by the capture device, while the rest is implemented by means of image processing. The set of descriptors is the following [7]: Motion Activity Descriptor (MAD), Camera Motion Descriptor (CMD), Motion Trajectory Descriptor (MTD), and Warp and

in 3D search engines and sketch-based modeling techniques. Some of the most popular 2D shape descriptors are curvature scale space (CSS) [13], SIFT [14], and SURF [15]. In fact, in the literature, 2D shape descriptors are classified into two main categories: contours and regions. Outline-based shape descriptors extract shape entities from the outline of a shape only. In contrast, region-based shape descriptors obtain shape characteristics of the entire region of a shape. In addition, hybrid techniques have also been proposed, combining techniques based on the

**2. The methods for extracting visual characteristics**

**4**

Image classification is an important step in the image recognition process. Indeed, many image classification techniques have been proposed to date. It is considered to be one of the main types of machine learning. Various studies have been carried out in order to choose the best technique for classifying [17] images.

### **3.1 What is machine learning?**

It is one of the subdomains of artificial intelligence (AI) which uses a series of techniques to let computers learn, (that is, gradually improve the performance of the computer on a task specific) with data, without being explicitly programmed. Indeed, machine learning covers a vast field of tasks. Below are the types of machine learning described in this section [18]:

**Supervised learning (classification):** In this case, the entries are tagged by an expert, and the algorithm must learn from the tags of these entries in order to predict the class of each new entry. In other words, from a set of observations X and another set of measures Y, we seek to estimate the dependencies between X and Y.

**Unsupervised learning (clustering):** In this case, the entries are not labeled, no expert is available, and the algorithm must predict the class of each entry. The objective of this type of learning is to describe how the data is organized and to extract homogeneous subsets.

**Semi-supervised learning:** the algorithm combines labeled and unlabeled examples to generate an appropriate function or class.

**Learning to learn:** where the algorithm learns its own inductive bias based on previous experience.


**4.3 The proposed alternative statistical interpretation for the informative graphical diagnostic**

As described in Step 1 of Section 4.2.1, the graphical representation of the modified winsorized means makes it possible to easily identify the predictor variable(s) whose variance is not significantly similar between the groups. If the variance of a predictor variable is not similar between groups, the bars representing the modified winsorized mean values for the variable in the 2-D area plot will not have similar shape. Such a variable is interpreted to be the identified variable that contains legitimate contaminants. This means that the more the shape of the bars differ between the groups, the easier it is to interpret the 2 D area plot. It is therefore necessary to provide an alternative interpretation when it is difficult to differentiate between a variable shape in the groups of the 2-D area plot. Therefore, a simple statistical or numerical interpretation is proposed for the informative graphical diagnosis when it is difficult to differentiate between a variable shape in the groups of the 2-D area plot. This alternative numerical interpretation consists of the following two simple steps:

*Step 1: Fitting the modified winsorized means values to a linear regression model.*

For each group, the modified winsorized means and their corresponding winsorized percentage values for each predictor variable are fitted to a linear regression model given as:

$$\begin{aligned} Y\_{11} &= a + b\_{11}X \\ &\vdots \\ Y\_{1P} &= a + b\_{1P}X \end{aligned} \tag{7}$$

and

$$\begin{aligned} Y\_{21} &= a + b\_{21}X \\ &\vdots \\ Y\_{2P} &= a + b\_{2P}X \end{aligned} \tag{8}$$

where, *Y*11, ⋯, *Y*1*P* and *Y*21, ⋯, *Y*2*P* are the modified winsorized mean values for the *P* predictor variables in groups 1 and 2, respectively, and *X* is the corresponding winsorized percent values.

*Step 2: Obtaining the absolute difference between the corresponding regression coefficients for the groups.*

The absolute difference between the obtained regression coefficients (i.e., the slope) in group 1 and 2 is computed as:

$$\delta\_{\rm abs} = b\_{1i} - b\_{2i}, i = 1, \cdots, p \tag{9}$$

where the subscripts 1 and 2 represents groups 1 and 2. The predictor variable that has an absolute difference of 0.75 or greater will be the variable identified with legitimate contaminants. In PDA, if two samples are equal in size, there is always a 50/50 chance. Most researchers would accept a classification accuracy of 25% greater than that caused by chance. Hence, the choice of 0.75 as the decision boundary.


*Article* **Methodology for Determining the Event-Based Taskload of an Air Traffic Controller Using Real-Time Simulations**

**María Zamarreño Suárez 1,\*, Rosa María Arnaldo Valdés 1, Francisco Pérez Moreno 1, Raquel Delgado-Aguilera Jurado 1, Patricia María López de Frutos 2 and Víctor Fernando Gómez Comendador 1**

**Abstract:** The study of human factors in aviation makes an important contribution to safety. Within this discipline, real-time simulations (RTS) are a very powerful tool. The use of simulators allows for exercises with controlled air traffic control (ATC) events to be designed so that their influence on the performance of air traffic controllers (ATCOs) can be studied. The CRITERIA (atC event-dRiven capacITy modEls foR aIr nAvigation) project aims to establish capacity models and determine the influence of a series of ATC events on the workload of ATCOs. To establish a correlation between these ATC events and neurophysiological variables, a previous step is needed: a methodology for defining the taskload faced by the ATCO during the development of each simulation. This paper presents the development of this methodology and a series of recommendations for extrapolating the lessons learnt from this line of research to similar experiments. This methodology starts from a taskload design, and after RTS and through the use of data related to the subjective evaluation of workload as an intermediate tool it allows the taskload profile experienced by the ATCO in each simulation to be defined. Six ATCO students participated in this experiment. They performed four exercises using the SkySim simulator. As an example, a case study of the analysis of one of the participants is presented.

**Keywords:** human factors; air traffic management; air traffic controllers; subjective workload assessment; air traffic control events; simulation platform; taskload; real-time simulations