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Noha Elfiky. (2009). Enhancing Local Binary Patterns with Spatial Pyramid Kernel: Application to Scene Classification (Vol. 129). Master's thesis, , Bellaterra, Barcelona.
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Hany Salah Eldeen. (2009). Colour Naming in Context through a Perceptual Model (Vol. 130). Master's thesis, , Bellaterra, Barcelona.
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Noha Elfiky, Fahad Shahbaz Khan, Joost Van de Weijer, & Jordi Gonzalez. (2012). Discriminative Compact Pyramids for Object and Scene Recognition. PR - Pattern Recognition, 45(4), 1627–1636.
Abstract: Spatial pyramids have been successfully applied to incorporating spatial information into bag-of-words based image representation. However, a major drawback is that it leads to high dimensional image representations. In this paper, we present a novel framework for obtaining compact pyramid representation. First, we investigate the usage of the divisive information theoretic feature clustering (DITC) algorithm in creating a compact pyramid representation. In many cases this method allows us to reduce the size of a high dimensional pyramid representation up to an order of magnitude with little or no loss in accuracy. Furthermore, comparison to clustering based on agglomerative information bottleneck (AIB) shows that our method obtains superior results at significantly lower computational costs. Moreover, we investigate the optimal combination of multiple features in the context of our compact pyramid representation. Finally, experiments show that the method can obtain state-of-the-art results on several challenging data sets.
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Hugo Jair Escalante, Heysem Kaya, Albert Ali Salah, Sergio Escalera, Yagmur Gucluturk, Umut Guçlu, et al. (2022). Modeling, Recognizing, and Explaining Apparent Personality from Videos. TAC - IEEE Transactions on Affective Computing, 13(2), 894–911.
Abstract: Explainability and interpretability are two critical aspects of decision support systems. Despite their importance, it is only recently that researchers are starting to explore these aspects. This paper provides an introduction to explainability and interpretability in the context of apparent personality recognition. To the best of our knowledge, this is the first effort in this direction. We describe a challenge we organized on explainability in first impressions analysis from video. We analyze in detail the newly introduced data set, evaluation protocol, proposed solutions and summarize the results of the challenge. We investigate the issue of bias in detail. Finally, derived from our study, we outline research opportunities that we foresee will be relevant in this area in the near future.
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Noha Elfiky, Jordi Gonzalez, & Xavier Roca. (2012). Compact and Adaptive Spatial Pyramids for Scene Recognition. IMAVIS - Image and Vision Computing, 30(8), 492–500.
Abstract: Most successful approaches on scenerecognition tend to efficiently combine global image features with spatial local appearance and shape cues. On the other hand, less attention has been devoted for studying spatial texture features within scenes. Our method is based on the insight that scenes can be seen as a composition of micro-texture patterns. This paper analyzes the role of texture along with its spatial layout for scenerecognition. However, one main drawback of the resulting spatial representation is its huge dimensionality. Hence, we propose a technique that addresses this problem by presenting a compactSpatialPyramid (SP) representation. The basis of our compact representation, namely, CompactAdaptiveSpatialPyramid (CASP) consists of a two-stages compression strategy. This strategy is based on the Agglomerative Information Bottleneck (AIB) theory for (i) compressing the least informative SP features, and, (ii) automatically learning the most appropriate shape for each category. Our method exceeds the state-of-the-art results on several challenging scenerecognition data sets.
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Sergio Escalera, Jordi Gonzalez, Hugo Jair Escalante, Xavier Baro, & Isabelle Guyon. (2018). Looking at People Special Issue. IJCV - International Journal of Computer Vision, 126(2-4), 141–143.
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Noha Elfiky, Theo Gevers, Arjan Gijsenij, & Jordi Gonzalez. (2014). Color Constancy using 3D Scene Geometry derived from a Single Image. TIP - IEEE Transactions on Image Processing, 23(9), 3855–3868.
Abstract: The aim of color constancy is to remove the effect of the color of the light source. As color constancy is inherently an ill-posed problem, most of the existing color constancy algorithms are based on specific imaging assumptions (e.g. grey-world and white patch assumption).
In this paper, 3D geometry models are used to determine which color constancy method to use for the different geometrical regions (depth/layer) found
in images. The aim is to classify images into stages (rough 3D geometry models). According to stage models; images are divided into stage regions using hard and soft segmentation. After that, the best color constancy methods is selected for each geometry depth. To this end, we propose a method to combine color constancy algorithms by investigating the relation between depth, local image statistics and color constancy. Image statistics are then exploited per depth to select the proper color constancy method. Our approach opens the possibility to estimate multiple illuminations by distinguishing
nearby light source from distant illuminations. Experiments on state-of-the-art data sets show that the proposed algorithm outperforms state-of-the-art
single color constancy algorithms with an improvement of almost 50% of median angular error. When using a perfect classifier (i.e, all of the test images are correctly classified into stages); the performance of the proposed method achieves an improvement of 52% of the median angular error compared to the best-performing single color constancy algorithm.
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Ivo Everts, Jan van Gemert, & Theo Gevers. (2013). Evaluation of Color STIPs for Human Action Recognition. In IEEE Conference on Computer Vision and Pattern Recognition (pp. 2850–2857).
Abstract: This paper is concerned with recognizing realistic human actions in videos based on spatio-temporal interest points (STIPs). Existing STIP-based action recognition approaches operate on intensity representations of the image data. Because of this, these approaches are sensitive to disturbing photometric phenomena such as highlights and shadows. Moreover, valuable information is neglected by discarding chromaticity from the photometric representation. These issues are addressed by Color STIPs. Color STIPs are multi-channel reformulations of existing intensity-based STIP detectors and descriptors, for which we consider a number of chromatic representations derived from the opponent color space. This enhanced modeling of appearance improves the quality of subsequent STIP detection and description. Color STIPs are shown to substantially outperform their intensity-based counterparts on the challenging UCF~sports, UCF11 and UCF50 action recognition benchmarks. Moreover, the results show that color STIPs are currently the single best low-level feature choice for STIP-based approaches to human action recognition.
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Ivo Everts, Jan van Gemert, & Theo Gevers. (2012). Per-patch Descriptor Selection using Surface and Scene Properties. In 12th European Conference on Computer Vision (Vol. 7577, pp. 172–186). LNCS. Springer Berlin Heidelberg.
Abstract: Local image descriptors are generally designed for describing all possible image patches. Such patches may be subject to complex variations in appearance due to incidental object, scene and recording conditions. Because of this, a single-best descriptor for accurate image representation under all conditions does not exist. Therefore, we propose to automatically select from a pool of descriptors the one that is best suitable based on object surface and scene properties. These properties are measured on the fly from a single image patch through a set of attributes. Attributes are input to a classifier which selects the best descriptor. Our experiments on a large dataset of colored object patches show that the proposed selection method outperforms the best single descriptor and a-priori combinations of the descriptor pool.
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Hugo Jair Escalante, Isabelle Guyon, Sergio Escalera, Julio C. S. Jacques Junior, Xavier Baro, Evelyne Viegas, et al. (2017). Design of an Explainable Machine Learning Challenge for Video Interviews. In International Joint Conference on Neural Networks.
Abstract: This paper reviews and discusses research advances on “explainable machine learning” in computer vision. We focus on a particular area of the “Looking at People” (LAP) thematic domain: first impressions and personality analysis. Our aim is to make the computational intelligence and computer vision communities aware of the importance of developing explanatory mechanisms for computer-assisted decision making applications, such as automating recruitment. Judgments based on personality traits are being made routinely by human resource departments to evaluate the candidates' capacity of social insertion and their potential of career growth. However, inferring personality traits and, in general, the process by which we humans form a first impression of people, is highly subjective and may be biased. Previous studies have demonstrated that learning machines can learn to mimic human decisions. In this paper, we go one step further and formulate the problem of explaining the decisions of the models as a means of identifying what visual aspects are important, understanding how they relate to decisions suggested, and possibly gaining insight into undesirable negative biases. We design a new challenge on explainability of learning machines for first impressions analysis. We describe the setting, scenario, evaluation metrics and preliminary outcomes of the competition. To the best of our knowledge this is the first effort in terms of challenges for explainability in computer vision. In addition our challenge design comprises several other quantitative and qualitative elements of novelty, including a “coopetition” setting, which combines competition and collaboration.
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Sergio Escalera, Jordi Gonzalez, Xavier Baro, Fernando Alonso, & Martha Mackay. (2016). Care Respite: a remote monitoring eHealth system for improving ambient assisted living. In Human Motion Analysis for Healthcare Applications.
Abstract: Advances in technology that capture human motion have been quite remarkable during the last five years. New sensors have been developed, such as the Microsoft Kinect, Asus Xtion Pro live, PrimeSense Carmine and Leap Motion. Their main advantages are their non-intrusive nature, low cost and widely available support for developers offered by large corporations or Open Communities. Although they were originally developed for computer games, they have inspired numerous healthcare related ideas and projects in areas such as Medical Disorder Diagnosis, Assisted Living, Rehabilitation and Surgery.
In Assisted Living, human motion analysis allows continuous monitoring of elderly and vulnerable people and their activities to potentially detect life-threatening events such as falls. Human motion analysis in rehabilitation provides the opportunity for motivating patients through gamification, evaluating prescribed programmes of exercises and assessing patients’ progress. In operating theatres, surgeons may use a gesture-based interface to access medical information or control a tele-surgery system. Human motion analysis may also be used to diagnose a range of mental and physical diseases and conditions.
This event will discuss recent advances in human motion sensing and provide an application to healthcare for networking and exploring potential synergies and collaborations.
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Sergio Escalera, Jordi Gonzalez, Xavier Baro, Pablo Pardo, Junior Fabian, Marc Oliu, et al. (2015). ChaLearn Looking at People 2015 new competitions: Age Estimation and Cultural Event Recognition. In IEEE International Joint Conference on Neural Networks IJCNN2015 (pp. 1–8).
Abstract: Following previous series on Looking at People (LAP) challenges [1], [2], [3], in 2015 ChaLearn runs two new competitions within the field of Looking at People: age and cultural event recognition in still images. We propose thefirst crowdsourcing application to collect and label data about apparent
age of people instead of the real age. In terms of cultural event recognition, tens of categories have to be recognized. This involves scene understanding and human analysis. This paper summarizes both challenges and data, providing some initial baselines. The results of the first round of the competition were presented at ChaLearn LAP 2015 IJCNN special session on computer vision and robotics http://www.dtic.ua.es/∼jgarcia/IJCNN2015.
Details of the ChaLearn LAP competitions can be found at http://gesture.chalearn.org/.
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Sergio Escalera, Jordi Gonzalez, Xavier Baro, Miguel Reyes, Oscar Lopes, Isabelle Guyon, et al. (2013). Multi-modal Gesture Recognition Challenge 2013: Dataset and Results. In 15th ACM International Conference on Multimodal Interaction (pp. 445–452).
Abstract: The recognition of continuous natural gestures is a complex and challenging problem due to the multi-modal nature of involved visual cues (e.g. fingers and lips movements, subtle facial expressions, body pose, etc.), as well as technical limitations such as spatial and temporal resolution and unreliable
depth cues. In order to promote the research advance on this field, we organized a challenge on multi-modal gesture recognition. We made available a large video database of 13; 858 gestures from a lexicon of 20 Italian gesture categories recorded with a KinectTM camera, providing the audio, skeletal model, user mask, RGB and depth images. The focus of the challenge was on user independent multiple gesture learning. There are no resting positions and the gestures are performed in continuous sequences lasting 1-2 minutes, containing between 8 and 20 gesture instances in each sequence. As a result, the dataset contains around 1:720:800 frames. In addition to the 20 main gesture categories, ‘distracter’ gestures are included, meaning that additional audio
and gestures out of the vocabulary are included. The final evaluation of the challenge was defined in terms of the Levenshtein edit distance, where the goal was to indicate the real order of gestures within the sequence. 54 international teams participated in the challenge, and outstanding results
were obtained by the first ranked participants.
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Sergio Escalera, Junior Fabian, Pablo Pardo, Xavier Baro, Jordi Gonzalez, Hugo Jair Escalante, et al. (2015). ChaLearn Looking at People 2015: Apparent Age and Cultural Event Recognition Datasets and Results. In 16th IEEE International Conference on Computer Vision Workshops (pp. 243–251).
Abstract: Following previous series on Looking at People (LAP) competitions [14, 13, 11, 12, 2], in 2015 ChaLearn ran two new competitions within the field of Looking at People: (1) age estimation, and (2) cultural event recognition, both in
still images. We developed a crowd-sourcing application to collect and label data about the apparent age of people (as opposed to the real age). In terms of cultural event recognition, one hundred categories had to be recognized. These
tasks involved scene understanding and human body analysis. This paper summarizes both challenges and data, as well as the results achieved by the participants of the competition.
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Sergio Escalera, Alicia Fornes, Oriol Pujol, Josep Llados, & Petia Radeva. (2011). Circular Blurred Shape Model for Multiclass Symbol Recognition. TSMCB - IEEE Transactions on Systems, Man and Cybernetics (Part B) (IEEE), 41(2), 497–506.
Abstract: In this paper, we propose a circular blurred shape model descriptor to deal with the problem of symbol detection and classification as a particular case of object recognition. The feature extraction is performed by capturing the spatial arrangement of significant object characteristics in a correlogram structure. The shape information from objects is shared among correlogram regions, where a prior blurring degree defines the level of distortion allowed in the symbol, making the descriptor tolerant to irregular deformations. Moreover, the descriptor is rotation invariant by definition. We validate the effectiveness of the proposed descriptor in both the multiclass symbol recognition and symbol detection domains. In order to perform the symbol detection, the descriptors are learned using a cascade of classifiers. In the case of multiclass categorization, the new feature space is learned using a set of binary classifiers which are embedded in an error-correcting output code design. The results over four symbol data sets show the significant improvements of the proposed descriptor compared to the state-of-the-art descriptors. In particular, the results are even more significant in those cases where the symbols suffer from elastic deformations.
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