Esteban J. Palomo

University of Malaga | UMA · Department of Computer Sciences and Languages

In this paper, a new hierarchical color quantization method based on self-organizing maps that provides different levels of quantization is presented. Color quantization (CQ) is a typical image processing task, which consists of selecting a small number of code vectors from a set of available colors to represent a high color resolution image with m...

Digital investigation methods are becoming more and more important due to the proliferation of digital crimes and crimes involving digital evidence. Network forensics is a research area that gathers evidence by collecting and analysing network traffic data logs. This analysis can be a difficult process, especially because of the high variability of...

Since the introduction of the growing hierarchical self-organizing map, much work has been done on self-organizing neural models with a dynamic structure. These models allow adjusting the layers of the model to the features of the input dataset. Here we propose a new self-organizing model which is based on a probabilistic mixture of multivariate Ga...

Coronary artery disease (CAD) remains the leading cause of death globally and invasive coronary angiography (ICA) is considered the gold standard of anatomical imaging evaluation when CAD is suspected. However, risk evaluation based on ICA has several limitations, such as visual assessment of stenosis severity, which has significant interobserver v...

Invasive Coronary Angiography (ICA) images are considered the gold standard for assessing the state of the coronary arteries. Deep learning classification methods are widely used and well-developed in different areas where medical imaging evaluation has an essential impact due to the development of computer-aided diagnosis systems that can support...

Video streams from panoramic cameras represent a powerful tool for automated surveillance systems, but naïve implementations typically require very intensive computational loads for applying deep learning models for automated detection and tracking of objects of interest, since these models require relatively high resolution to reliably perform obj...

The dilemma between stability and plasticity is crucial in machine learning, especially when non-stationary input distributions are considered. This issue can be addressed by continual learning in order to alleviate catastrophic forgetting. This strategy has been previously proposed for supervised and reinforcement learning models. However, little...

Knowing student learning styles represents an effective way to design the most suitable methodology for our students so that performance can improve with less effort for both students and teachers. However, a methodology is usually set in teaching guides according to the previous academic year's information without any knowledge of our current audi...

Continual learning tries to address the stability-plasticity dilemma to avoid catastrophic forgetting when dealing with non-stationary distributions. Prior works focused on supervised or reinforcement learning, but few works have considered continual learning for unsupervised learning methods. In this paper, a novel approach to provide continual le...

The evaluation methods employed in a course are the most important point for the students, above any other learning aspect. For teachers, this task is arduous when the number of students is high. Traditional evaluation requires the teacher to grade all the assignments and exams, while peer assessments have become a valuable tool to involve students...

A learning style describes what are the predominant skills for learning tasks. In the context of university education, knowing the learning styles of the students constitutes a great opportunity to improve both teaching and evaluation. By using the Honey-Alonso Learning Styles Questionnaire (CHAEA), in this work, we carried out a longitudinal study...

In this paper, a new color quantization method based on a self-organized artificial neural network called the Growing Hierarchical Bregman Neural Gas (GHBNG) is proposed. This neural network is based on Bregman divergences, from which the squared Euclidean distance is a particular case. Thus, the best suitable Bregman divergence for color quantizat...

In this paper, a new self-organizing artificial neural network called growing hierarchical neural forest (GHNF) is proposed. The GHNF is a hierarchical model based on the growing neural forest, which is a tree-based model that learns a set of trees (forest) instead of a general graph so that the forest can grow in size. This way, the GHNF faces thr...

The design of automated video surveillance systems often involves the detection of agents which exhibit anomalous or dangerous behavior in the scene under analysis. Models aimed to enhance the video pattern recognition abilities of the system are commonly integrated in order to increase its performance. Deep learning neural networks are found among...

Sleep disorders is one of the most frequent child medical consultation, indeed the rate of children that suffer it in a transitory way is considerably high. Among the most common sleep disorders is named “children behavioral insomnia“, many different drugs has been used as treatment with poor results with relevant secondary effects. We focus on chi...

Automatic video surveillance systems are usually designed to detect anomalous objects being present in a scene or behaving dangerously. In order to perform adequately, they must incorporate models able to achieve accurate pattern recognition in an image, and deep learning neural networks excel at this task. However, exhaustive scan of the full imag...

Self-organizing maps (SOMs) are aimed to learn a representation of the input distribution which faithfully describes the topological relations among the clusters of the distribution. For some data sets and applications, it is known beforehand that some regions of the input space cannot contain any samples. Those are known as forbidden regions. In t...

Pan-Tilt-zoom (PTZ) cameras are well suited to motion detection and tracking objects due to their mobility. Motion detection approaches based on background difference have been the most used with fixed cameras because of the high quality of the achieved segmentation. However, time requirements and high costs prevent most of the algorithms proposed...

Automatic video surveillance systems are usually designed to detect anomalous objects being present in a scene or behaving dangerously. In order to perform adequately, they must incorporate models able to achieve accurate pattern recognition in an image, and deep learning neural networks excel at this task. However, exhaustive scan of the full imag...

Background segmentation methods are exposed to the effects of different kinds of noise due to the limitations of image acquisition devices. This type of distortion can worsen the performance of segmentation methods because the input pixel values are altered. In this paper we study how several well-known background segmentation methods perform when...

Most clustering algorithms are designed to minimize a distortion measure which quantifies how far the elements of the clusters are from their respective centroids. The assessment of the results is often carried out with the help of cluster quality measures which take into account the compactness and separation of the clusters. However, these measur...

The Self-Organizing Map model considers the possibility of 1D and 3D map topologies. However, 2D maps are by far the most used in practice. Moreover, there is a lack of a theory which studies the relative merits of 1D, 2D and 3D maps. In this paper a theory of this kind is developed, which can be used to assess which topologies are better suited fo...

Motion detection is the first essential process in the extraction of information regarding moving objects. The approaches based on background difference are the most used with fixed cameras to perform motion detection, because of the high quality of the achieved segmentation. However, real time requirements and high costs prevent most of the algori...

Image processing has become a very common application for artificial intelligence-based algorithms. More precisely, color quantization has become an important issue when it comes to supply efficient transmission and storage for digital images, which consists of color indexing for minimal perceptual distortion image compression. Artificial Neural Ne...

The discovery of the underlying topology of real-world data is a difficult task due to the high-dimensional and the complex structure in real datasets. In some cases, when the topology of the data is not known or the information is provided in a stream, it is advantageous to learn tree topologies from the data. This task can be carried out by dynam...

Among current foreground detection algorithms for video sequences, methods based on self-organizing maps are obtaining a greater relevance. In this work we propose a probabilistic self-organising map based model, which uses a uniform distribution to represent the foreground. A suitable set of characteristic pixel features is chosen to train the pro...

A frame resolution reduction framework to reduce the computational load and improve the foreground detection in video sequences is presented in this work. The proposed framework consists of three different stages. Firstly, the original video frame is downsampled using a specific interpolation function. Secondly, a foreground detection of the reduce...

Electronic noses are sensing devices able to classify chemical volatiles according to the readings of an array of non-selective gas sensors and some pattern recognition algorithm. Given their high versatility to host multiple sensors while still being compact and lightweight, e-noses have demonstrated to be a promising technology to real-world chem...

Most current approaches to computer vision are based on expensive, high performance hardware to meet the heavy computational requirements of the employed algorithms. These system architectures are severely limited in their practical application due to financial and technical limitations. In this work a different strategy is used, namely the develop...

The growing neural gas (GNG) self-organizing neural network stands as one of the most successful examples of unsupervised learning of a graph of processing units. Despite its success, little attention has been devoted to its extension to a hierarchical model, unlike other models such as the self-organizing map, which has many hierarchical versions....

In this work, a novel self-organizing model called growing neural forest (GNF) is presented. It is based on the growing neural gas (GNG), which learns a general graph with no special provisions for datasets with separated clusters. On the contrary, the proposed GNF learns a set of trees so that each tree represents a connected cluster of data. High...

Visualization of real world data is a difficult task due to the high-dimensional and the complex structure in real datasets. Scientific data visualization requires a variety of mathematical techniques to transform high-dimensional data sets into simple graphical objects that provide a clearer understanding. In this work a Self-Organizing Spanning T...

Detecting foreground objects on scenes is a fundamental task in computer vision and the used color space is an important election for this task. In many situations, especially on dynamic backgrounds, neither grayscale nor RGB color spaces represent the best solution to detect foreground objects. Other standard color spaces, such as YCbCr or HSV, ha...

Most of the work done on self-organizing maps relies on the minimization of the mean squared error. This nonrobust approach leads to poor performance in the presence of outliers. Here we consider robust M-estimators as an alternative for least squares in the context of self-organization. New learning rules are derived, so that the original Kohonen'...

The obtaining of perfect foreground segmentation masks still remains as a challenging task in video surveillance systems, since errors in that initial stage could lead to misleadings in subsequent tasks as object tracking and behavior analysis. This work presents a novel methodology based on self-organizing neural networks and Gaussian distribution...

Growing hierarchical self-organizing models are characterized by the flexibility of their structure, which can easily accomodate for complex input datasets. However, most proposals use the Euclidean distance as the only error measure. Here we propose a way to introduce Bregman divergences in these models, which is based on stochastic approximation...

The classification of land usage in mountain grassland bovine areas is important for the management of forage production and grazing in grass-based livestock systems. The present paper proposes a novel, hierarchical neural network-based approach towards the classification of land usage in these areas. A survey of 72 farms was conducted in the Massi...

This paper studies the reliability of geometric features for the identification of users based on hand biometrics. Our methodology is based on genetic algorithms and mutual information. The aim is to provide a system for user identification rather than a classification. Additionally, a robust hand segmentation method to extract the hand silhouette...

Most of object detection algorithms do not yield perfect foreground segmentation masks. These errors in the initial stage of video surveillance systems could cause that the subsequent tasks like object tracking and behavior analysis, can be extremely compromised. In this paper, we propose a methodology based on self-organizing neural networks and h...

Image segmentation is a typical task in the field of image processing. There is a great number of image segmentation methods in the literature, but most of these methods are not suitable for multispectral images and they require a priori knowledge. In this work, a hierarchical self-organizing network is proposed for multispectral image segmentation...

Both image compression based on color quantization and image segmentation are two typical tasks in the field of image processing. Several techniques based on splitting algorithms or cluster analyses have been proposed in the literature. Self-organizing maps have been also applied to these problems, although with some limitations due to the fixed ne...

This paper presents an ART-type network (adaptive resonant theory) to detect ob-jects in a video sequence classifying the pixels as foreground or background. The proposed ART network (ART+) not only possesses the structure and learning abil-ity of an ART-based network, but also uses a neural merging process to adapt the variability of the input dat...

Tracking of moving objects in real situation is a challenging research issue, due to dynamic changes in objects or background appearance, illumination, shape and occlusions. In this paper, we deal with these difficulties by incorporating an adaptive feature weighting mechanism to the proposed growing competitive neural network for multiple objects...

This chapter presents a novel methodology for using feature selection in hand biometric systems, based on genetic algorithms and mutual information. The aim is to provide a standard features dataset which diminishes the number of features to extract and decreases the complexity of the whole identification process. The experimental results show that...

Current research is improving the quality and efficiency of digital investigation methods due to the continuous proliferation of digital crimes. This includes the use of software tools that can help with digital investigations. A novel method for the analysis and visualisation of network forensics traffic data, based on growing hierarchical self-or...

Self-organizing neural networks are usually focused on prototype learning, while the topology is held fixed during the learning process. Here a method to adapt the topology of the network so that it reflects the internal structure of the input distribution is proposed. This leads to a self-organizing graph, where each unit is a mixture component of...

In this paper, a novel methodology for using feature selection in hand biometric systems, based on genetic algorithms and mutual information is presented. A hand seg-mentation algorithm based on adaptive threshold and active contours is also applied, in order to deal with complex back-grounds and non-homogeneous illumination. The aim of this method...

Self-Organizing Maps (SOM) have some problems related to its fixed topology and its lack of representation of hierarchical relations among input data. Growing Hierarchical SOMs (GHSOM) solve these limitations by generating a hier- archical architecture that is automatically determined according to the input data and reflects the inherent hierarchic...

Digital crimes are a part of modern life but evidence of these crimes can be captured in network traffic data logs. Analysing these logs is a difficult process, this is especially true as the format that different attacks can take can vary tremendously and may be unknown at the time of the analysis. The main objective of the field of network forens...

Object tracking in video sequences remains as one of the most challenging problems in computer vision. Object occlusion, sudden trajectory changes and other difficulties still wait for comprehensive solutions. Here we propose a feature weighting method which is able to discover the most relevant features for this task, and a competitive learning ne...

A new approach for image compression based on the GHSOM model has been proposed in this paper. The SOM has some problems related to its fixed topology and its lack of representation of hierarchical relations among input data. The GHSOM solves these limitations by generating a hierarchical architecture that is automatically determined according to t...

An anomaly detection system based on a hierarchical self-organizing neural network is presented. The proposed neural network reduces the amount of parameters that a user should define prior to the training to a single parameter. This allows the network to perform more autonomously while maintaining a good performance, which is less dependent on the...

In this work, a hierarchical self-organizing model based on the GHSOM is presented in order to cluster web contents. The GHSOM is an artificial neural network that has been widely used for data clustering. The hierarchical architecture of the GHSOM is more flexible than a single SOM since it is adapted to input data, mirroring inherent hierarchical...

A novel approach for image segmentation is proposed in this paper. This approach is based on the growing hierarchical self-organizing map (GHSOM), which consists of a hierarchical architecture composed of growing self-organizing maps (SOMs). The SOMs have shown to be successful for the analysis of high-dimensional input data as in data mining appli...

The GHSOM is an artificial neural network that has been widely used for data clustering. The hierarchical architecture of the GHSOM is more flexible than a single SOM since it is adapted to input data, mirroring inherent hierarchical relations among them. The adaptation process of the GHSOM architecture is controlled by two parameters. However, the...

A Growing Competitive Neural Network system is presented as a precise method to track moving objects for video-surveillance. The number of neurons in this neural model can be automatically increased or decreased in order to get a one-to-one association between objects currently in the scene and neurons. This association is kept in each frame, what...

The self-organizing map (SOM) has been used in multiple areas and constitutes an excellent tool for data mining. However, SOM has two main drawbacks: the static architecture and the lack of representation of hierarchical relations among input data. The growing hierarchical SOM (GHSOM) was proposed in order to face these difficulties. The network ar...

This paper describes a multiagent system with capabilities to analyze and discover knowledge gathered from distributed agents. These enhanced capabilities are obtained through a dynamic self-organizing map and a multiagent communication system. The central administrator agent dynamically obtains information about the attacks or intrusions from the...

The aim of this paper is to present the use of Growing Competitive Neural Networks as a precise method to track moving objects for video-surveillance. The number of neurons in this neural model can be automatically increased or decreased in order to get a one-to-one association between objects currently in the scene and neurons. This association is...

This paper presents a hierarchical self-organizing neural network for intrusion detection. The proposed neural model consists of a hierarchical architecture composed of independent growing self-organizing maps (SOMs). The SOMs have shown to be successful for the analysis of high-dimensional input data as in data mining applications such as network...

This paper presents a hierarchical self-organizing neural network for intrusion detection. The proposed neural model consists of a hierarchical architecture composed of independent growing self-organizing maps (SOMs). The SOMs have shown to be successful for the analysis of high-dimensional input data as in data mining applications such as network...

This paper presents a neural recognition system for manufacturing applications in difficult industrial environments. In such difficult environments, where objects to be recognized can be dirty and illumination conditions cannot be sufficiently controlled, the required accuracy and rigidity of the system are critical features. The purpose of the rea...

An intrusion detection system (IDS) monitors the IP packets flowing over the network to capture intrusions or anomalies. One of the techniques used for anomaly detection is building statistical models using metrics derived from observation of the user's actions. In this paper, a neural network model based on self organization is proposed for detect...

An intrusion detection system (IDS) monitors the IP packets flowing over the network to capture intrusions or anomalies. One of the techniques used for anomaly detection is building statistical models using metrics derived from observation of the user’s actions. A neural network model based on self organization is proposed for detecting intrusions....

The aim of this work is to present a segmentation method to detect moving objects in video scenes, based on the use of a multivalued discrete neural network to improve the results obtained by an underlying segmentation algorithm. Specifically, the multivalued neural model (MREM) is used to detect and correct some of the deficiencies and errors off...

This paper present a video segmentation method which separate pixels corresponding to foreground from those corresponding to background. The proposed background model consists of a competitive neural network based on dipoles, which is used to classify the pixels as background or foreground. Using this kind of neural networks permits an easy hardwar...

This work proposes an unsupervised competitive neural network based on adaptive neighborhoods for video segmentation and object detection. The designed neural network is proposed to form a background model based on subtraction approach. The synaptic weights and the adaptive neighborhood of the neurons serve as a model of the background and are upda...