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Sergio Escalera, Oriol Pujol, & Petia Radeva. (2007). Robust Complex Salient Regions. In 3rd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2007), J. Marti et al. (Eds.) LNCS 4478:113–121.
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Sergio Escalera, Oriol Pujol, & Petia Radeva. (2008). Sub-Class Error-Correcting Output Codes. In Computer Vision Systems. 6th International Conference (Vol. 5008, 494–504).
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Sergio Escalera, Oriol Pujol, Eric Laciar, Jordi Vitria, Esther Pueyo, & Petia Radeva. (2010). Classification of Coronary Damage in Chronic Chagasic Patients. In M. H.(eds) V. Sgurev (Ed.), Intelligent Systems – From Theory to Practice. Studies in Computational Intelligence (Vol. 299, pp. 461–478). Springer-Verlag.
Abstract: Post Conference IEEE-IS 2008
The Chagas’ disease is endemic in all Latin America, affecting millions of people in the continent. In order to diagnose and treat the chagas’ disease, it is important to detect and measure the coronary damage of the patient. In this paper,
we analyze and categorize patients into different groups based on the coronary damage produced by the disease. Based on the features of the heart cycle extracted using high resolution ECG, a multi-class scheme of Error-Correcting Output Codes (ECOC)is formulated and successfully applied. The results show that the proposed scheme obtains significant performance improvements compared to previous works and state-of-the-art ECOC designs.
Keywords: Chagas disease; Error-Correcting Output Codes; High resolution ECG; Decoding
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Sergio Escalera, Marti Soler, Stephane Ayache, Umut Guçlu, Jun Wan, Meysam Madadi, et al. (2019). ChaLearn Looking at People: Inpainting and Denoising Challenges. In The Springer Series on Challenges in Machine Learning (pp. 23–44).
Abstract: Dealing with incomplete information is a well studied problem in the context of machine learning and computational intelligence. However, in the context of computer vision, the problem has only been studied in specific scenarios (e.g., certain types of occlusions in specific types of images), although it is common to have incomplete information in visual data. This chapter describes the design of an academic competition focusing on inpainting of images and video sequences that was part of the competition program of WCCI2018 and had a satellite event collocated with ECCV2018. The ChaLearn Looking at People Inpainting Challenge aimed at advancing the state of the art on visual inpainting by promoting the development of methods for recovering missing and occluded information from images and video. Three tracks were proposed in which visual inpainting might be helpful but still challenging: human body pose estimation, text overlays removal and fingerprint denoising. This chapter describes the design of the challenge, which includes the release of three novel datasets, and the description of evaluation metrics, baselines and evaluation protocol. The results of the challenge are analyzed and discussed in detail and conclusions derived from this event are outlined.
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Sergio Escalera, Markus Weimer, Mikhail Burtsev, Valentin Malykh, Varvara Logacheva, Ryan Lowe, et al. (2018). Introduction to NIPS 2017 Competition Track. In Sergio Escalera, & Markus Weimer (Eds.), The NIPS ’17 Competition: Building Intelligent Systems (pp. 1–23). Springer.
Abstract: Competitions have become a popular tool in the data science community to solve hard problems, assess the state of the art and spur new research directions. Companies like Kaggle and open source platforms like Codalab connect people with data and a data science problem to those with the skills and means to solve it. Hence, the question arises: What, if anything, could NIPS add to this rich ecosystem?
In 2017, we embarked to find out. We attracted 23 potential competitions, of which we selected five to be NIPS 2017 competitions. Our final selection features competitions advancing the state of the art in other sciences such as “Classifying Clinically Actionable Genetic Mutations” and “Learning to Run”. Others, like “The Conversational Intelligence Challenge” and “Adversarial Attacks and Defences” generated new data sets that we expect to impact the progress in their respective communities for years to come. And “Human-Computer Question Answering Competition” showed us just how far we as a field have come in ability and efficiency since the break-through performance of Watson in Jeopardy. Two additional competitions, DeepArt and AI XPRIZE Milestions, were also associated to the NIPS 2017 competition track, whose results are also presented within this chapter.
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Sergio Escalera, David M.J. Tax, Oriol Pujol, Petia Radeva, & Robert P.W. Duin. (2011). Multi-Class Classification in Image Analysis Via Error-Correcting Output Codes. In H. Kawasnicka, & L.Jain (Eds.), Innovations in Intelligent Image Analysis (Vol. 339, pp. 7–29). Berlin: Springer Berlin Heidelberg.
Abstract: A common way to model multi-class classification problems is by means of Error-Correcting Output Codes (ECOC). Given a multi-class problem, the ECOC technique designs a codeword for each class, where each position of the code identifies the membership of the class for a given binary problem.A classification decision is obtained by assigning the label of the class with the closest code. In this paper, we overview the state-of-the-art on ECOC designs and test them in real applications. Results on different multi-class data sets show the benefits of using the ensemble of classifiers when categorizing objects in images.
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Santiago Segui, Laura Igual, Petia Radeva, Carolina Malagelada, Fernando Azpiroz, & Jordi Vitria. (2007). A Semi-Supervised Learning Method for Motility Disease Diagnostic. In Progress in Pattern Recognition, Image Analysis and Applications, 12th Iberoamerican Congress on Pattern (CIARP 2007), LCNS 4756:773–782, ISBN 978–3–540–76724–4.
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Santiago Segui, Laura Igual, Fernando Vilariño, Petia Radeva, Carolina Malagelada, Fernando Azpiroz, et al. (2008). Diagnostic System for Intestinal Motility Disfunctions Using Video Capsule Endoscopy. In and J.K. Tsotsos M. V. A. Gasteratos (Ed.), Computer Vision Systems. 6th International (Vol. 5008, 251–260). LNCS. Berlin Heidelberg: Springer-Verlag.
Abstract: Wireless Video Capsule Endoscopy is a clinical technique consisting of the analysis of images from the intestine which are pro- vided by an ingestible device with a camera attached to it. In this paper we propose an automatic system to diagnose severe intestinal motility disfunctions using the video endoscopy data. The system is based on the application of computer vision techniques within a machine learn- ing framework in order to obtain the characterization of diverse motil- ity events from video sequences. We present experimental results that demonstrate the effectiveness of the proposed system and compare them with the ground-truth provided by the gastroenterologists.
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Salvatore Tabbone, & Oriol Ramos Terrades. (2014). An Overview of Symbol Recognition. In D. Doermann, & K. Tombre (Eds.), Handbook of Document Image Processing and Recognition (Vol. D, pp. 523–551). Springer London.
Abstract: According to the Cambridge Dictionaries Online, a symbol is a sign, shape, or object that is used to represent something else. Symbol recognition is a subfield of general pattern recognition problems that focuses on identifying, detecting, and recognizing symbols in technical drawings, maps, or miscellaneous documents such as logos and musical scores. This chapter aims at providing the reader an overview of the different existing ways of describing and recognizing symbols and how the field has evolved to attain a certain degree of maturity.
Keywords: Pattern recognition; Shape descriptors; Structural descriptors; Symbolrecognition; Symbol spotting
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Salim Jouili, Salvatore Tabbone, & Ernest Valveny. (2010). Comparing Graph Similarity Measures for Graphical Recognition. In Graphics Recognition. Achievements, Challenges, and Evolution. 8th International Workshop, GREC 2009. Selected Papers (Vol. 6020, pp. 37–48). LNCS. Springer Berlin Heidelberg.
Abstract: In this paper we evaluate four graph distance measures. The analysis is performed for document retrieval tasks. For this aim, different kind of documents are used including line drawings (symbols), ancient documents (ornamental letters), shapes and trademark-logos. The experimental results show that the performance of each graph distance measure depends on the kind of data and the graph representation technique.
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Raul Gomez, Lluis Gomez, Jaume Gibert, & Dimosthenis Karatzas. (2019). Self-Supervised Learning from Web Data for Multimodal Retrieval. In Multi-Modal Scene Understanding Book (pp. 279–306).
Abstract: Self-Supervised learning from multimodal image and text data allows deep neural networks to learn powerful features with no need of human annotated data. Web and Social Media platforms provide a virtually unlimited amount of this multimodal data. In this work we propose to exploit this free available data to learn a multimodal image and text embedding, aiming to leverage the semantic knowledge learnt in the text domain and transfer it to a visual model for semantic image retrieval. We demonstrate that the proposed pipeline can learn from images with associated text without supervision and analyze the semantic structure of the learnt joint image and text embeddingspace. Weperformathoroughanalysisandperformancecomparisonoffivedifferentstateof the art text embeddings in three different benchmarks. We show that the embeddings learnt with Web and Social Media data have competitive performances over supervised methods in the text basedimageretrievaltask,andweclearlyoutperformstateoftheartintheMIRFlickrdatasetwhen training in the target data. Further, we demonstrate how semantic multimodal image retrieval can be performed using the learnt embeddings, going beyond classical instance-level retrieval problems. Finally, we present a new dataset, InstaCities1M, composed by Instagram images and their associated texts that can be used for fair comparison of image-text embeddings.
Keywords: self-supervised learning; webly supervised learning; text embeddings; multimodal retrieval; multimodal embedding
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Rain Eric Haamer, Eka Rusadze, Iiris Lusi, Tauseef Ahmed, Sergio Escalera, & Gholamreza Anbarjafari. (2018). Review on Emotion Recognition Databases. In Human-Robot Interaction: Theory and Application.
Abstract: Over the past few decades human-computer interaction has become more important in our daily lives and research has developed in many directions: memory research, depression detection, and behavioural deficiency detection, lie detection, (hidden) emotion recognition etc. Because of that, the number of generic emotion and face databases or those tailored to specific needs have grown immensely large. Thus, a comprehensive yet compact guide is needed to help researchers find the most suitable database and understand what types of databases already exist. In this paper, different elicitation methods are discussed and the databases are primarily organized into neat and informative tables based on the format.
Keywords: emotion; computer vision; databases
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Quan-sen Sun, Zhong Jin, Pheng-ann Heng, & De-shen Xia. (2005). A novel feature fusion method based on partial least squares regression. In Pattern Recognition and Data Mining, Lecture Notes in Computer Science, 3686: 268–277.
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Quan-sen Sun, Pheng-ann Heng, Zhong Jin, & De-shen Xia. (2005). Face recognition based on generalized canonical correlation analysis. In Advances in Intelligent Computing, Lecture Notes in Computer Science, 3645: 958–967.
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Philippe Dosch, & Ernest Valveny. (2006). Report on the Second Symbol Recognition Contest. In Graphics Recognition: Ten Years Review and Future Perspectives, W. Liu, J. Llados (Eds.), LNCS 3926: 381–397.
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