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Isabelle Guyon; Lisheng Sun Hosoya; Marc Boulle; Hugo Jair Escalante; Sergio Escalera; Zhengying Liu; Damir Jajetic; Bisakha Ray; Mehreen Saeed; Michele Sebag; Alexander R.Statnikov; Wei-Wei Tu; Evelyne Viegas |
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Title |
Analysis of the AutoML Challenge Series 2015-2018. |
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2019 |
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Automated Machine Learning |
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177-219 |
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The ChaLearn AutoML Challenge (The authors are in alphabetical order of last name, except the first author who did most of the writing and the second author who produced most of the numerical analyses and plots.) (NIPS 2015 – ICML 2016) consisted of six rounds of a machine learning competition of progressive difficulty, subject to limited computational resources. It was followed bya one-round AutoML challenge (PAKDD 2018). The AutoML setting differs from former model selection/hyper-parameter selection challenges, such as the one we previously organized for NIPS 2006: the participants aim to develop fully automated and computationally efficient systems, capable of being trained and tested without human intervention, with code submission. This chapter analyzes the results of these competitions and provides details about the datasets, which were not revealed to the participants. The solutions of the winners are systematically benchmarked over all datasets of all rounds and compared with canonical machine learning algorithms available in scikit-learn. All materials discussed in this chapter (data and code) have been made publicly available at http://automl.chalearn.org/. |
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HuPBA; no proj |
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Admin @ si @ GHB2019 |
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3330 |
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Debora Gil; Oriol Ramos Terrades; Raquel Perez |
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Title |
Topological Radiomics (TOPiomics): Early Detection of Genetic Abnormalities in Cancer Treatment Evolution |
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2021 |
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Extended Abstracts GEOMVAP 2019, Trends in Mathematics 15 |
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15 |
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89–93 |
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Abnormalities in radiomic measures correlate to genomic alterations prone to alter the outcome of personalized anti-cancer treatments. TOPiomics is a new method for the early detection of variations in tumor imaging phenotype from a topological structure in multi-view radiomic spaces. |
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Springer Nature |
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IAM; DAG; 600.120; 600.145; 600.139 |
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Admin @ si @ GRP2021 |
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3594 |
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Debora Gil; F. Javier Sanchez; Gloria Fernandez Esparrach; Jorge Bernal |
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Title |
3D Stable Spatio-temporal Polyp Localization in Colonoscopy Videos |
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Book Chapter |
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2015 |
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Computer-Assisted and Robotic Endoscopy. Revised selected papers of Second International Workshop, CARE 2015, Held in Conjunction with MICCAI 2015 |
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9515 |
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140-152 |
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Colonoscopy, Polyp Detection, Polyp Localization, Region Extraction, Watersheds |
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Computational intelligent systems could reduce polyp miss rate in colonoscopy for colon cancer diagnosis and, thus, increase the efficiency of the procedure. One of the main problems of existing polyp localization methods is a lack of spatio-temporal stability in their response. We propose to explore the response of a given polyp localization across temporal windows in order to select
those image regions presenting the highest stable spatio-temporal response.
Spatio-temporal stability is achieved by extracting 3D watershed regions on the
temporal window. Stability in localization response is statistically determined by analysis of the variance of the output of the localization method inside each 3D region. We have explored the benefits of considering spatio-temporal stability in two different tasks: polyp localization and polyp detection. Experimental results indicate an average improvement of 21:5% in polyp localization and 43:78% in polyp detection. |
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CARE |
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IAM; MV; 600.075 |
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Admin @ si @ GSF2015 |
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2733 |
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Pedro Herruzo; Marc Bolaños; Petia Radeva |
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Title |
Can a CNN Recognize Catalan Diet? |
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2016 |
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AIP Conference Proceedings |
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1773 |
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CoRR abs/1607.08811
Nowadays, we can find several diseases related to the unhealthy diet habits of the population, such as diabetes, obesity, anemia, bulimia and anorexia. In many cases, these diseases are related to the food consumption of people. Mediterranean diet is scientifically known as a healthy diet that helps to prevent many metabolic diseases. In particular, our work focuses on the recognition of Mediterranean food and dishes. The development of this methodology would allow to analise the daily habits of users with wearable cameras, within the topic of lifelogging. By using automatic mechanisms we could build an objective tool for the analysis of the patient’s behavior, allowing specialists to discover unhealthy food patterns and understand the user’s lifestyle.
With the aim to automatically recognize a complete diet, we introduce a challenging multi-labeled dataset related to Mediter-ranean diet called FoodCAT. The first type of label provided consists of 115 food classes with an average of 400 images per dish, and the second one consists of 12 food categories with an average of 3800 pictures per class. This dataset will serve as a basis for the development of automatic diet recognition. In this context, deep learning and more specifically, Convolutional Neural Networks (CNNs), currently are state-of-the-art methods for automatic food recognition. In our work, we compare several architectures for image classification, with the purpose of diet recognition. Applying the best model for recognising food categories, we achieve a top-1 accuracy of 72.29%, and top-5 of 97.07%. In a complete diet recognition of dishes from Mediterranean diet, enlarged with the Food-101 dataset for international dishes recognition, we achieve a top-1 accuracy of 68.07%, and top-5 of 89.53%, for a total of 115+101 food classes. |
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MILAB |
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no |
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Admin @ si @ HBR2016 |
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2837 |
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Author |
Lluis Pere de las Heras; David Fernandez; Alicia Fornes; Ernest Valveny; Gemma Sanchez; Josep Llados |
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Title |
Runlength Histogram Image Signature for Perceptual Retrieval of Architectural Floor Plans |
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Book Chapter |
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Year |
2014 |
Publication |
Graphics Recognition. Current Trends and Challenges |
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8746 |
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135-146 |
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Graphics recognition; Graphics retrieval; Image classification |
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This paper proposes a runlength histogram signature as a perceptual descriptor of architectural plans in a retrieval scenario. The style of an architectural drawing is characterized by the perception of lines, shapes and texture. Such visual stimuli are the basis for defining semantic concepts as space properties, symmetry, density, etc. We propose runlength histograms extracted in vertical, horizontal and diagonal directions as a characterization of line and space properties in floorplans, so it can be roughly associated to a description of walls and room structure. A retrieval application illustrates the performance of the proposed approach, where given a plan as a query, similar ones are obtained from a database. A ground truth based on human observation has been constructed to validate the hypothesis. Additional retrieval results on sketched building’s facades are reported qualitatively in this paper. Its good description and its adaptability to two different sketch drawings despite its simplicity shows the interest of the proposed approach and opens a challenging research line in graphics recognition. |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-662-44853-3 |
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DAG; ADAS; 600.045; 600.056; 600.061; 600.076; 600.077 |
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Admin @ si @ HFF2014 |
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2536 |
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Lluis Pere de las Heras; Joan Mas; Gemma Sanchez; Ernest Valveny |
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Title |
Notation-invariant patch-based wall detector in architectural floor plans |
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2013 |
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Graphics Recognition. New Trends and Challenges |
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7423 |
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79--88 |
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Architectural floor plans exhibit a large variability in notation. Therefore, segmenting and identifying the elements of any kind of plan becomes a challenging task for approaches based on grouping structural primitives obtained by vectorization. Recently, a patch-based segmentation method working at pixel level and relying on the construction of a visual vocabulary has been proposed in [1], showing its adaptability to different notations by automatically learning the visual appearance of the elements in each different notation. This paper presents an evolution of that previous work, after analyzing and testing several alternatives for each of the different steps of the method: Firstly, an automatic plan-size normalization process is done. Secondly we evaluate different features to obtain the description of every patch. Thirdly, we train an SVM classifier to obtain the category of every patch instead of constructing a visual vocabulary. These variations of the method have been tested for wall detection on two datasets of architectural floor plans with different notations. After studying in deep each of the steps in the process pipeline, we are able to find the best system configuration, which highly outperforms the results on wall segmentation obtained by the original paper. |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-36823-3 |
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DAG; 600.045; 600.056; 605.203 |
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Admin @ si @ HMS2013 |
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2322 |
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Author |
Lluis Pere de las Heras; Oriol Ramos Terrades; Josep Llados |
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Title |
Ontology-Based Understanding of Architectural Drawings |
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Book Chapter |
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Year |
2017 |
Publication |
International Workshop on Graphics Recognition. GREC 2015.Graphic Recognition. Current Trends and Challenges |
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9657 |
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75-85 |
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Graphics recognition; Floor plan analysi; Domain ontology |
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In this paper we present a knowledge base of architectural documents aiming at improving existing methods of floor plan classification and understanding. It consists of an ontological definition of the domain and the inclusion of real instances coming from both, automatically interpreted and manually labeled documents. The knowledge base has proven to be an effective tool to structure our knowledge and to easily maintain and upgrade it. Moreover, it is an appropriate means to automatically check the consistency of relational data and a convenient complement of hard-coded knowledge interpretation systems. |
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DAG; 600.121 |
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Admin @ si @ HRL2017 |
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3086 |
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Rain Eric Haamer; Eka Rusadze; Iiris Lusi; Tauseef Ahmed; Sergio Escalera; Gholamreza Anbarjafari |
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Title |
Review on Emotion Recognition Databases |
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2018 |
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Human-Robot Interaction: Theory and Application |
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emotion; computer vision; databases |
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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. |
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978-1-78923-316-2 |
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HUPBA; 602.133 |
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Admin @ si @ HRL2018 |
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3212 |
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Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades |
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Title |
Spotting Symbol over Graphical Documents Via Sparsity in Visual Vocabulary |
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2016 |
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Recent Trends in Image Processing and Pattern Recognition |
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709 |
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RTIP2R |
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DAG |
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Admin @ si @ HTR2016 |
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2956 |
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Lluis Pere de las Heras; Ernest Valveny; Gemma Sanchez |
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Unsupervised and Notation-Independent Wall Segmentation in Floor Plans Using a Combination of Statistical and Structural Strategies |
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2014 |
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Graphics Recognition. Current Trends and Challenges |
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8746 |
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109-121 |
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Graphics recognition; Floor plan analysis; Object segmentation |
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In this paper we present a wall segmentation approach in floor plans that is able to work independently to the graphical notation, does not need any pre-annotated data for learning, and is able to segment multiple-shaped walls such as beams and curved-walls. This method results from the combination of the wall segmentation approaches [3, 5] presented recently by the authors. Firstly, potential straight wall segments are extracted in an unsupervised way similar to [3], but restricting even more the wall candidates considered in the original approach. Then, based on [5], these segments are used to learn the texture pattern of walls and spot the lost instances. The presented combination of both methods has been tested on 4 available datasets with different notations and compared qualitatively and quantitatively to the state-of-the-art applied on these collections. Additionally, some qualitative results on floor plans directly downloaded from the Internet are reported in the paper. The overall performance of the method demonstrates either its adaptability to different wall notations and shapes, and to document qualities and resolutions. |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-662-44853-3 |
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DAG; ADAS; 600.076; 600.077 |
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Admin @ si @ HVS2014 |
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2535 |
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Laura Igual; Joan Carles Soliva; Antonio Hernandez; Sergio Escalera; Oscar Vilarroya; Petia Radeva |
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A Supervised Graph-cut Deformable Model for Brain MRI Segmentation. Deformation models: tracking, animation and applications |
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2012 |
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Computational Vision and Biomechanics |
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Springer Netherlands |
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MILAB;HuPBA |
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Admin @ si @ ISH2012b |
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2066 |
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Zhong Jin; Franck Davoine; Zhen Lou; Jing-Yu Yang |
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A novel PCA-based Bayes classifier and face analysis |
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2006 |
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International Conference on Advances in Biometrics (ICB’06), LNCS 3832: 144–150 |
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Hong Kong |
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Admin @ si @ JDL2006 |
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624 |
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Hana Jarraya; Muhammad Muzzamil Luqman; Jean-Yves Ramel |
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Improving Fuzzy Multilevel Graph Embedding Technique by Employing Topological Node Features: An Application to Graphics Recognition |
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2017 |
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Graphics Recognition. Current Trends and Challenges |
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9657 |
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Springer |
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B. Lamiroy; R Dueire Lins |
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GREC |
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DAG; 600.097; 600.121 |
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Admin @ si @ JLR2017 |
Serial |
2928 |
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Author |
Salim Jouili; Salvatore Tabbone; Ernest Valveny |
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Title |
Comparing Graph Similarity Measures for Graphical Recognition |
Type |
Book Chapter |
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Year |
2010 |
Publication |
Graphics Recognition. Achievements, Challenges, and Evolution. 8th International Workshop, GREC 2009. Selected Papers |
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Volume |
6020 |
Issue |
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Pages |
37-48 |
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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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Publisher |
Springer Berlin Heidelberg |
Place of Publication |
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Original Title |
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LNCS |
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Series Issue |
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Edition |
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ISSN |
0302-9743 |
ISBN |
978-3-642-13727-3 |
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Conference |
GREC |
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Notes |
DAG |
Approved |
no |
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Call Number |
Admin @ si @ JTV2010 |
Serial |
2404 |
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Author |
A.Kesidis; Dimosthenis Karatzas |
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Title |
Logo and Trademark Recognition |
Type |
Book Chapter |
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Year |
2014 |
Publication |
Handbook of Document Image Processing and Recognition |
Abbreviated Journal |
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Volume |
D |
Issue |
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Pages |
591-646 |
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Keywords |
Logo recognition; Logo removal; Logo spotting; Trademark registration; Trademark retrieval systems |
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Abstract |
The importance of logos and trademarks in nowadays society is indisputable, variably seen under a positive light as a valuable service for consumers or a negative one as a catalyst of ever-increasing consumerism. This chapter discusses the technical approaches for enabling machines to work with logos, looking into the latest methodologies for logo detection, localization, representation, recognition, retrieval, and spotting in a variety of media. This analysis is presented in the context of three different applications covering the complete depth and breadth of state of the art techniques. These are trademark retrieval systems, logo recognition in document images, and logo detection and removal in images and videos. This chapter, due to the very nature of logos and trademarks, brings together various facets of document image analysis spanning graphical and textual content, while it links document image analysis to other computer vision domains, especially when it comes to the analysis of real-scene videos and images. |
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Publisher |
Springer London |
Place of Publication |
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Editor |
D. Doermann; K. Tombre |
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Original Title |
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Series Editor |
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Edition |
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ISSN |
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ISBN |
978-0-85729-858-4 |
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Notes |
DAG; 600.077 |
Approved |
no |
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Call Number |
Admin @ si @ KeK2014 |
Serial |
2425 |
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| Permanent link to this record |
