ABSTRACT As benchmark image datasets expand in sample size and feature complexity, the challenge of managing increased dimensionality becomes apparent. Contrary to the expectation that more features equate to enhanced information and improved outcomes, the curse of dimensionality often hampers performance. This paper reviews existing literature on filter feature selection techniques applied to image features, highlighting their use in both classical and deep‐learning‐based feature extraction methods. Building on these findings, this study proposes a scalable approach for image feature extraction and selection using Big Data technologies, specifically Apache Spark, to efficiently process large and high‐dimensional datasets. The proposed framework integrates filter‐based feature selection methods within a distributed environment to evaluate their effectiveness in image analysis tasks. Several experiments were performed to compare the results using feature selection techniques with various reduction percentages. Results show that significant feature reduction can be achieved without compromising classification accuracy, demonstrating the potential of Spark‐based distributed processing for large‐scale image analytics.

Dimensionality reduction techniques are essential for visualizing and analyzing high-dimensional data.This study explores the impact of distance measures on the performance of Uniform Manifold Approximation and Projection (UMAP), a widely used dimensionality reduction method.We evaluate their influence on cluster separation, structure preservation, and their effectiveness when used as a preprocessing step for classification tasks on real and synthetic datasets.The results highlight the importance of tailoring distance measures to specific data contexts and provide guidance for optimizing UMAP applications.

Abstract The growing prevalence of nutrition-related health conditions calls for advanced tools to support reliable and efficient dietary monitoring. This paper presents NutriConv, a lightweight multitask convolutional neural network designed to simultaneously perform food classification and weight estimation from single-item food images. Trained on the institutionally validated PANCAKE dataset from the European Food Safety Authority, NutriConv combines classification and regression objectives within a unified architecture, optimized via a hybrid loss function. While its classification accuracy remains lower than that of specialized single-task models, NutriConv achieves competitive regression performance and offers a practical balance between both tasks. Its compact design enables deployment on resource-constrained platforms such as smartglasses and mobile health devices, expanding its usability in real-world dietary tracking scenarios. Extensive experiments confirm its robustness, including external validation on the Nutrition5K dataset, underscoring the model’s generalizability. This work highlights the potential of multitask learning for integrated, scalable, and accessible AI-based nutrition assessment.

In the fast-evolving field of artificial intelligence, where models are increasingly growing in complexity and size, the availability of labeled data for training deep learning models has become a significant challenge. Addressing complex problems like object detection demands considerable time and resources for data labeling to achieve meaningful results. For companies developing such applications, this entails extensive investment in highly skilled personnel or costly outsourcing. This research work aims to demonstrate that enhancing feature extractors can substantially alleviate this challenge, enabling models to learn more effective representations with less labeled data. Utilizing a self-supervised learning strategy, we present a model trained on unlabeled data that outperforms state-of-the-art feature extractors pre-trained on ImageNet and particularly designed for object detection tasks. Moreover, the results demonstrate that our approach encourages the model to focus on the most relevant aspects of an object, thus achieving better feature representations and, therefore, reinforcing its reliability and robustness.

Computer vision syndrome causes vision problems and discomfort mainly due to dry eye. Several studies show that dry eye in computer users is caused by a reduction in the blink rate and an increase in the prevalence of incomplete blinks. In this context, this article introduces Eye-LRCN, a new eye blink detection method that also evaluates the completeness of the blink. The method is based on a long-term recurrent convolutional network (LRCN), which combines a convolutional neural network (CNN) for feature extraction with a bidirectional recurrent neural network that performs sequence learning and classifies the blinks. A Siamese architecture is used during CNN training to overcome the high-class imbalance present in blink detection and the limited amount of data available to train blink detection models. The method was evaluated on three different tasks: blink detection, blink completeness detection, and eye state detection. We report superior performance to the state-of-the-art methods in blink detection and blink completeness detection, and remarkable results in eye state detection.

Recommender Systems (RS) are based on the generalization of the observed interactions of a population of users with a collection of items. Collaborative Filters (CF) give good results, but they degrade when there are few interactions to learn from. The alternative would be to observe some features of the users that could be linked to their tastes. However, specific information on users or items is often not available. In this research work, we explore how to exploit the photos of items taken by users. Our aim is to assign similar meanings to the photos of items with which the same group of users interacted. For this purpose, we define a multi-label classification task from images to sets of users. The classifier uses a general-purpose convolutional neural network to extract the basic visual features, followed by additional layers necessary to accomplish the learning task. To evaluate our proposal we compared it with CFs, using two tourism datasets that include: restaurants of six cities and points of interest of three locations. According to the experimentation carried out, the poor results achieved by CFs are outperformed by our proposal, which takes into account the visual and taste semantics of the available photos.

Abstract Navigation through large volumes of images is a complex and tedious task that requires tools to facilitate the exploration and discovery of visual information. Photo summaries are one of these tools, which consist of selecting a reduced set of images that best represent the original data source. However, creating photo summaries in the context of recommender systems poses several challenges: How to select the most relevant images for each item? How to encode each image? How to evaluate the quality of the generated summary? In this manuscript, we propose a clustering-based method to create a visual summary in the context of a restaurant recommender system, which includes the photos taken by users who visited the restaurants (items) in a given city. These photos are encoded using a deep neural network that takes into account not only their content but also the relationships between users and restaurants. This encoding will allow us to create a visual summary that captures the essence of user tastes and illustrates the gastronomic offer of the city. We also propose a similarity measure between items based on the users who have visited them and an evaluation method that calculates to what extent the summary obtained represents the original data source. The experimentation carried out includes five datasets and the obtained results demonstrate the adequacy of our proposal for the construction of these summaries.

Many machine learning and data mining tasks are based on distance measures, so a large amount of literature addresses this aspect somehow. Due to the broad scope of the topic, this paper aims to provide an overview of the use of these measures in the most common machine learning problems, pointing out those aspects to consider to choose the most appropriate measure for a particular task. For this purpose, the most recent works addressing the subject were reviewed and seven of the most commonly used measures were analyzed, investigating in detail their main properties and applications. Different experiments were carried out to study their relationships and compare their performance. The degradation of the results in the presence of noise was also considered, as well as the execution time required by each measure.

Recommender systems suggest products to users, based on their popularity or the users' preferences.This paper proposes a hybrid personalized recommender system based on users' tastes and also on information available about items.We used a dataset downloaded from Tri-pAdvisor, which contains some information from restaurants (items), such as price range or special diets.Feature selection techniques are employed to analyze the impact that each variable has on personalized recommendations, allowing us to understand not only the process underlying the recommendation to favor the transparency of the system, but also what users value the most when choosing a restaurant.

Several research studies have demonstrated the strong influence that online reviews exert on consumers' purchasing decisions. Specifically, those with extreme opinions, both favorable and unfavorable, are often considered more useful. This paper is focused on enhancing the detection of extreme reviews through sentiment analysis. For this purpose, a real scenario is taken into account, using the examples of all classes and dealing with the imbalance between them, which is characteristic in online reviews. The main objective is to carry out the classification with a high certainty and incurring as few errors as possible in relation to the examples belonging to the rest of the classes. Therefore, the emphasis is on the quality of the predictions rather than on the quantity. Using XLNet, we show how the transfer of knowledge extracted from the source domain (i.e., the extreme reviews) improves their detection regarding the overall number of errors made in the target domain (i.e., multi-class classification).

The increase in awareness of people towards their nutritional habits has drawn considerable attention to the field of automatic food analysis. Focusing on self-service restaurants environment, automatic food analysis is not only useful for extracting nutritional information from foods selected by customers, it is also of high interest to speed up the service solving the bottleneck produced at the cashiers in times of high demand. In this paper, we address the problem of automatic food tray analysis in canteens and restaurants environment, which consists in predicting multiple foods placed on a tray image. We propose a new approach for food analysis based on convolutional neural networks, we name Semantic Food Detection, which integrates in the same framework food localization, recognition and segmentation. We demonstrate that our method improves the state of the art food detection by a considerable margin on the public dataset UNIMIB2016 achieving about 90% in terms of F-measure, and thus provides a significant technological advance towards the automatic billing in restaurant environments.

Image quality assessment has been a topic of intense research over the last decades. Although its application to other disciplines is growing tremendously, its use in retinal imaging is still immature and some fundamental challenges remain unsolved. Thus, we present a research methodology for the objective assessment of the quality in retinal images. The methodology can be used as a preliminary step in any computer-aided system, and is composed of four main steps: the location of the region-of-interest, the extraction of relevant image properties and their analysis by feature selection, and the final binary classification into two classes (good and poor quality). The experimental results demonstrate the adequacy of the proposed methodology in this context, being able to objectively assess the quality of retinal images with an accuracy over 99%.

Dry eye syndrome is recognized as a growing health problem, and one of the most frequent reasons for seeking eye care. Its etiology and management challenge clinicians and researchers alike, and several clinical tests can be used to diagnose it. One of the most frequently used tests is the evaluation of the interference patterns of the tear film lipid layer. Based on this clinical test, this paper presents CASDES, a computer-aided system to support the diagnosis of dry eye syndrome. Furthermore, CASDES is also useful to support the diagnosis of other eye diseases, such as meibomian gland dysfunction, since it provides a tear film map with highly useful information for eye practitioners. Experiments demonstrate the robustness of this novel tool, which outperforms the previous attempts to create tear film maps and provides reliable results in comparison with the clinicians' annotations. Note that the processing time is noticeably reduced with the proposed method, which will help to promote its clinical use in the diagnosis and treatment of dry eye.

The proliferation of high-dimensional data in the last few years has brought a necessity to use dimensionality reduction techniques, in which feature selection is arguably the most famous one. Feature selection consists of detecting relevant features and discarding the irrelevant ones. However, there are some situations where the users are not only interested in the relevance of the selected features but also in the costs that they imply (e.g. economical or computational costs). In this paper an extension of the well-known ReliefF method for feature selection is proposed, which consists of adding a new term to the function which updates the weights of the features so as to be able to reach a trade-off between the relevance of a feature and its associated cost. The behavior of the proposed method is tested on twelve heterogeneous classification datasets as well as a real application, using a support vector machine (SVM) as a classifier. The results of the experimental study show that the approach is sound, since it allows the user to reduce the cost significantly without compromising the classification error.

Dry eye syndrome is characterized by symptoms of discomfort, ocular surface damage, reduced tear film stability, and tear hyperosmolarity. These features can be identified by several types of diagnostic tests, although there may not be a direct correlation between the severity of symptoms and the degree of damage. One of the most used clinical tests is the analysis of the lipid interference patterns, which can be observed on the tear film, and their classification into the Guillon categories. Our previous researches have demonstrated that the interference patterns can be characterized as color texture patterns. Thus, the manual test done by experts can be performed through an automatic process which saves time for experts and provides unbiased results. Nevertheless, the heterogeneity of the tear film makes the classification of a patient's image into a single category impossible. For this reason, this paper presents a methodology to create tear film maps based on the lipid interference patterns. In this way, the output image represents the distribution and prevalence of the Guillon categories on the tear film. The adequacy of the proposed methodology was demonstrated since it achieves reliable results in comparison with the annotations done by experts.

Dry eye is a common disease which affects a large portion of the population and harms their routine activities. Its diagnosis and monitoring require a battery of tests, each designed for different aspects. One of these clinical tests measures the quality of the tear film and is based on its appearance, which can be observed using the Doane interferometer. The manual process done by experts consists of classifying the interferometry images into one of the five categories considered. The variability existing in these images makes necessary the use of an automatic system for supporting dry eye diagnosis. In this research, a methodology to perform this classification automatically is presented. This methodology includes a color and texture analysis of the images, and also the use of feature selection methods to reduce image processing time. The effectiveness of the proposed methodology was demonstrated since it provides unbiased results with classification errors lower than 9%. Additionally, it saves time for experts and can work in real-time for clinical purposes.

Dry eye is a symptomatic disease which affects a wide range of population and has a negative impact on their daily activities. Its diagnosis can be achieved by analyzing the interference patterns of the tear film lipid layer and by classifying them into one of the Guillon categories. The manual process done by experts is not only affected by subjective factors but is also very time consuming. In this paper we propose a general methodology to the automatic classification of tear film lipid layer, using color and texture information to characterize the image and feature selection methods to reduce the processing time. The adequacy of the proposed methodology was demonstrated since it achieves classification rates over 97% while maintaining robustness and provides unbiased results. Also, it can be applied in real time, and so allows important time savings for the experts.

In this work Optical Coherence Tomography (OCT) retinal images are automatically processed to detect the presence of cysts. The methodology is composed by three phases: region of interest where cysts will be searched is delimited; a watershed algorithm is applied to find all the possible regions in the image which might conform cystic structures; finally, texture analysis is performed in each region from previous phase to final classification. Results show that accuracy achieved with this method is over 80%.

Dry eye is a symptomatic disease which affects a wide range of population and has a negative impact on their daily activities, such as driving or working with computers. Its diagnosis can be achieved by several clinical tests, one of which is the analysis of the interference pattern and its classification into one of the Guillon's categories. The methodologies for automatic classification obtain promising results but at the expense of requiring a long processing time. In this research, feature selection techniques are used to reduce time whilst maintaining performance, paving the way for the development of a novel tool for automatic classification of tear film lipid layer. This tool produces significant classification rates over 96% compared with the annotations of the optometrists and provides unbiased results. Also, it works in real-time and so allows important time savings for the experts.