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	Author	Hugo Jair Escalante; Sergio Escalera; Isabelle Guyon; Xavier Baro; Yagmur Gucluturk; Umut Guçlu; Marcel van Gerven
	Title	Explainable and Interpretable Models in Computer Vision and Machine Learning			Type	Book Whole
	Year	2018	Publication	The Springer Series on Challenges in Machine Learning	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	This book compiles leading research on the development of explainable and interpretable machine learning methods in the context of computer vision and machine learning. Research progress in computer vision and pattern recognition has led to a variety of modeling techniques with almost human-like performance. Although these models have obtained astounding results, they are limited in their explainability and interpretability: what is the rationale behind the decision made? what in the model structure explains its functioning? Hence, while good performance is a critical required characteristic for learning machines, explainability and interpretability capabilities are needed to take learning machines to the next step to include them in decision support systems involving human supervision. This book, written by leading international researchers, addresses key topics of explainability and interpretability, including the following: ·Evaluation and Generalization in Interpretable Machine Learning ·Explanation Methods in Deep Learning ·Learning Functional Causal Models with Generative Neural Networks ·Learning Interpreatable Rules for Multi-Label Classification ·Structuring Neural Networks for More Explainable Predictions ·Generating Post Hoc Rationales of Deep Visual Classification Decisions ·Ensembling Visual Explanations ·Explainable Deep Driving by Visualizing Causal Attention ·Interdisciplinary Perspective on Algorithmic Job Candidate Search ·Multimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisions ·Inherent Explainability Pattern Theory-based Video Event Interpretations
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	HuPBA; no menciona			Approved	no
	Call Number	Admin @ si @ EEG2018			Serial	3399
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	Author	Albert Clapes
	Title	Learning to recognize human actions: from hand-crafted to deep-learning based visual representations			Type	Book Whole
	Year	2019	Publication	PhD Thesis, Universitat de Barcelona-CVC	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Action recognition is a very challenging and important problem in computer vision. Researchers working on this field aspire to provide computers with the abil ity to visually perceive human actions – that is, to observe, interpret, and under stand human-related events that occur in the physical environment merely from visual data. The applications of this technology are numerous: human-machine interaction, e-health, monitoring/surveillance, and content-based video retrieval, among others. Hand-crafted methods dominated the field until the apparition of the first successful deep learning-based action recognition works. Although ear lier deep-based methods underperformed with respect to hand-crafted approaches, these slowly but steadily improved to become state-of-the-art, eventually achieving better results than hand-crafted ones. Still, hand-crafted approaches can be advan tageous in certain scenarios, specially when not enough data is available to train very large deep models or simply to be combined with deep-based methods to fur ther boost the performance. Hence, showing how hand-crafted features can provide extra knowledge the deep networks are notable to easily learn about human actions. This Thesis concurs in time with this change of paradigm and, hence, reflects it into two distinguished parts. In the first part, we focus on improving current suc cessful hand-crafted approaches for action recognition and we do so from three dif ferent perspectives. Using the dense trajectories framework as a backbone: first, we explore the use of multi-modal and multi-view input data to enrich the trajectory de scriptors. Second, we focus on the classification part of action recognition pipelines and propose an ensemble learning approach, where each classifier leams from a different set of local spatiotemporal features to then combine their outputs following an strategy based on the Dempster-Shaffer Theory. And third, we propose a novel hand-crafted feature extraction method that constructs a rnid-level feature descrip tion to better modellong-term spatiotemporal dynarnics within action videos. Moving to the second part of the Thesis, we start with a comprehensive study of the current deep-learning based action recognition methods. We review both fun damental and cutting edge methodologies reported during the last few years and introduce a taxonomy of deep-leaming methods dedicated to action recognition. In particular, we analyze and discuss how these handle the temporal dimension of data. Last but not least, we propose a residual recurrent network for action recogni tion that naturally integrates all our previous findings in a powerful and prornising framework.
	Address	January 2019
	Corporate Author				Thesis	Ph.D. thesis
	Publisher	Ediciones Graficas Rey	Place of Publication		Editor	Sergio Escalera
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-84-948531-2-8	Medium
	Area		Expedition		Conference
	Notes	HUPBA			Approved	no
	Call Number	Admin @ si @ Cla2019			Serial	3219
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	Author	Victor Ponce
	Title	Evolutionary Bags of Space-Time Features for Human Analysis			Type	Book Whole
	Year	2016	Publication	PhD Thesis Universitat de Barcelona, UOC and CVC	Abbreviated Journal
	Volume		Issue		Pages
	Keywords	Computer algorithms; Digital image processing; Digital video; Analysis of variance; Dynamic programming; Evolutionary computation; Gesture
	Abstract	The representation (or feature) learning has been an emerging concept in the last years, since it collects a set of techniques that are present in any theoretical or practical methodology referring to artificial intelligence. In computer vision, a very common representation has adopted the form of the well-known Bag of Visual Words. This representation appears implicitly in most approaches where images are described, and is also present in a huge number of areas and domains: image content retrieval, pedestrian detection, human-computer interaction, surveillance, e-health, and social computing, amongst others. The early stages of this dissertation provide an approach for learning visual representations inside evolutionary algorithms, which consists of evolving weighting schemes to improve the BoVW representations for the task of recognizing categories of videos and images. Thus, we demonstrate the applicability of the most common weighting schemes, which are often used in text mining but are less frequently found in computer vision tasks. Beyond learning these visual representations, we provide an approach based on fusion strategies for learning spatiotemporal representations, from multimodal data obtained by depth sensors. Besides, we specially aim at the evolutionary and dynamic modelling, where the temporal factor is present in the nature of the data, such as video sequences of gestures and actions. Indeed, we explore the effects of probabilistic modelling for those approaches based on dynamic programming, so as to handle the temporal deformation and variance amongst video sequences of different categories. Finally, we integrate dynamic programming and generative models into an evolutionary computation framework, with the aim of learning Bags of SubGestures (BoSG) representations and hence to improve the generalization capability of standard gesture recognition approaches. The results obtained in the experimentation demonstrate, first, that evolutionary algorithms are useful for improving the representation of BoVW approaches in several datasets for recognizing categories in still images and video sequences. On the other hand, our experimentation reveals that both, the use of dynamic programming and generative models to align video sequences, and the representations obtained from applying fusion strategies in multimodal data, entail an enhancement on the performance when recognizing some gesture categories. Furthermore, the combination of evolutionary algorithms with models based on dynamic programming and generative approaches results, when aiming at the classification of video categories on large video datasets, in a considerable improvement over standard gesture and action recognition approaches. Finally, we demonstrate the applications of these representations in several domains for human analysis: classification of images where humans may be present, action and gesture recognition for general applications, and in particular for conversational settings within the field of restorative justice
	Address	June 2016
	Corporate Author				Thesis	Ph.D. thesis
	Publisher	Ediciones Graficas Rey	Place of Publication		Editor	Sergio Escalera;Xavier Baro;Hugo Jair Escalante
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	HuPBA			Approved	no
	Call Number	Pon2016			Serial	2814
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	Author	Meysam Madadi
	Title	Human Segmentation, Pose Estimation and Applications			Type	Book Whole
	Year	2017	Publication	PhD Thesis, Universitat Autonoma de Barcelona-CVC	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Automatic analyzing humans in photographs or videos has great potential applications in computer vision, including medical diagnosis, sports, entertainment, movie editing and surveillance, just to name a few. Body, face and hand are the most studied components of humans. Body has many variabilities in shape and clothing along with high degrees of freedom in pose. Face has many muscles causing many visible deformity, beside variable shape and hair style. Hand is a small object, moving fast and has high degrees of freedom. Adding human characteristics to all aforementioned variabilities makes human analysis quite a challenging task. In this thesis, we developed human segmentation in different modalities. In a first scenario, we segmented human body and hand in depth images using example-based shape warping. We developed a shape descriptor based on shape context and class probabilities of shape regions to extract nearest neighbors. We then considered rigid affine alignment vs. nonrigid iterative shape warping. In a second scenario, we segmented face in RGB images using convolutional neural networks (CNN). We modeled conditional random field with recurrent neural networks. In our model pair-wise kernels are not fixed and learned during training. We trained the network end-to-end using adversarial networks which improved hair segmentation by a high margin. We also worked on 3D hand pose estimation in depth images. In a generative approach, we fitted a finger model separately for each finger based on our example-based rigid hand segmentation. We minimized an energy function based on overlapping area, depth discrepancy and finger collisions. We also applied linear models in joint trajectory space to refine occluded joints based on visible joints error and invisible joints trajectory smoothness. In a CNN-based approach, we developed a tree-structure network to train specific features for each finger and fused them for global pose consistency. We also formulated physical and appearance constraints as loss functions. Finally, we developed a number of applications consisting of human soft biometrics measurement and garment retexturing. We also generated some datasets in this thesis consisting of human segmentation, synthetic hand pose, garment retexturing and Italian gestures.
	Address	October 2017
	Corporate Author				Thesis	Ph.D. thesis
	Publisher	Ediciones Graficas Rey	Place of Publication		Editor	Sergio Escalera;Jordi Gonzalez
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-84-945373-3-2	Medium
	Area		Expedition		Conference
	Notes	HUPBA			Approved	no
	Call Number	Admin @ si @ Mad2017			Serial	3017
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	Author	Gholamreza Anbarjafari; Sergio Escalera
	Title	Human-Robot Interaction: Theory and Application			Type	Book Whole
	Year	2018	Publication	Human-Robot Interaction: Theory and Application	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-1-78923-316-2	Medium
	Area		Expedition		Conference
	Notes	HUPBA			Approved	no
	Call Number	Admin @ si @ AnE2018			Serial	3216
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	Author	Jun Wan; Guodong Guo; Sergio Escalera; Hugo Jair Escalante; Stan Z. Li
	Title	Multi-modal Face Presentation Attach Detection			Type	Book Whole
	Year	2020	Publication	Synthesis Lectures on Computer Vision	Abbreviated Journal
	Volume	13	Issue		Pages
	Keywords
	Abstract
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	HuPBA			Approved	no
	Call Number	Admin @ si @ WGE2020			Serial	3440
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	Author	Jun Wan; Guodong Guo; Sergio Escalera; Hugo Jair Escalante; Stan Z Li
	Title	Advances in Face Presentation Attack Detection			Type	Book Whole
	Year	2023	Publication	Advances in Face Presentation Attack Detection	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	HUPBA			Approved	no
	Call Number	Admin @ si @ WGE2023a			Serial	3955
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	Author	Juan Ignacio Toledo
	Title	Information Extraction from Heterogeneous Handwritten Documents			Type	Book Whole
	Year	2019	Publication	PhD Thesis, Universitat Autonoma de Barcelona-CVC	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	In this thesis we explore information Extraction from totally or partially handwritten documents. Basically we are dealing with two different application scenarios. The first scenario are modern highly structured documents like forms. In this kind of documents, the semantic information is encoded in different fields with a pre-defined location in the document, therefore, information extraction becomes roughly equivalent to transcription. The second application scenario are loosely structured totally handwritten documents, besides transcribing them, we need to assign a semantic label, from a set of known values to the handwritten words. In both scenarios, transcription is an important part of the information extraction. For that reason in this thesis we present two methods based on Neural Networks, to transcribe handwritten text.In order to tackle the challenge of loosely structured documents, we have produced a benchmark, consisting of a dataset, a defined set of tasks and a metric, that was presented to the community as an international competition. Also, we propose different models based on Convolutional and Recurrent neural networks that are able to transcribe and assign different semantic labels to each handwritten words, that is, able to perform Information Extraction.
	Address	July 2019
	Corporate Author				Thesis	Ph.D. thesis
	Publisher	Ediciones Graficas Rey	Place of Publication		Editor	Alicia Fornes;Josep Llados
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-84-948531-7-3	Medium
	Area		Expedition		Conference
	Notes	DAG; 600.140; 600.121			Approved	no
	Call Number	Admin @ si @ Tol2019			Serial	3389
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	Author	Albert Berenguel
	Title	Analysis of background textures in banknotes and identity documents for counterfeit detection			Type	Book Whole
	Year	2019	Publication	PhD Thesis, Universitat Autonoma de Barcelona-CVC	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Counterfeiting and piracy are a form of theft that has been steadily growing in recent years. A counterfeit is an unauthorized reproduction of an authentic/genuine object. Banknotes and identity documents are two common objects of counterfeiting. The former is used by organized criminal groups to finance a variety of illegal activities or even to destabilize entire countries due the inflation effect. Generally, in order to run their illicit businesses, counterfeiters establish companies and bank accounts using fraudulent identity documents. The illegal activities generated by counterfeit banknotes and identity documents has a damaging effect on business, the economy and the general population. To fight against counterfeiters, governments and authorities around the globe cooperate and develop security features to protect their security documents. Many of the security features in identity documents can also be found in banknotes. In this dissertation we focus our efforts in detecting the counterfeit banknotes and identity documents by analyzing the security features at the background printing. Background areas on secure documents contain fine-line patterns and designs that are difficult to reproduce without the manufacturers cutting-edge printing equipment. Our objective is to find the loose of resolution between the genuine security document and the printed counterfeit version with a publicly available commercial printer. We first present the most complete survey to date in identity and banknote security features. The compared algorithms and systems are based on computer vision and machine learning. Then we advance to present the banknote and identity counterfeit dataset we have built and use along all this thesis. Afterwards, we evaluate and adapt algorithms in the literature for the security background texture analysis. We study this problem from the point of view of robustness, computational efficiency and applicability into a real and non-controlled industrial scenario, proposing key insights to use these algorithms. Next, within the industrial environment of this thesis, we build a complete service oriented architecture to detect counterfeit documents. The mobile application and the server framework intends to be used even by non-expert document examiners to spot counterfeits. Later, we re-frame the problem of background texture counterfeit detection as a full-reference game of spotting the differences, by alternating glimpses between a counterfeit and a genuine background using recurrent neural networks. Finally, we deal with the lack of counterfeit samples, studying different approaches based on anomaly detection.
	Address	November 2019
	Corporate Author				Thesis	Ph.D. thesis
	Publisher	Ediciones Graficas Rey	Place of Publication		Editor	Oriol Ramos Terrades;Josep Llados
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-84-121011-2-6	Medium
	Area		Expedition		Conference
	Notes	DAG; 600.140; 600.121			Approved	no
	Call Number	Admin @ si @ Ber2019			Serial	3395
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	Author	Dena Bazazian
	Title	Fully Convolutional Networks for Text Understanding in Scene Images			Type	Book Whole
	Year	2018	Publication	PhD Thesis, Universitat Autonoma de Barcelona-CVC	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Text understanding in scene images has gained plenty of attention in the computer vision community and it is an important task in many applications as text carries semantically rich information about scene content and context. For instance, reading text in a scene can be applied to autonomous driving, scene understanding or assisting visually impaired people. The general aim of scene text understanding is to localize and recognize text in scene images. Text regions are first localized in the original image by a trained detector model and afterwards fed into a recognition module. The tasks of localization and recognition are highly correlated since an inaccurate localization can affect the recognition task. The main purpose of this thesis is to devise efficient methods for scene text understanding. We investigate how the latest results on deep learning can advance text understanding pipelines. Recently, Fully Convolutional Networks (FCNs) and derived methods have achieved a significant performance on semantic segmentation and pixel level classification tasks. Therefore, we took benefit of the strengths of FCN approaches in order to detect text in natural scenes. In this thesis we have focused on two challenging tasks of scene text understanding which are Text Detection and Word Spotting. For the task of text detection, we have proposed an efficient text proposal technique in scene images. We have considered the Text Proposals method as the baseline which is an approach to reduce the search space of possible text regions in an image. In order to improve the Text Proposals method we combined it with Fully Convolutional Networks to efficiently reduce the number of proposals while maintaining the same level of accuracy and thus gaining a significant speed up. Our experiments demonstrate that this text proposal approach yields significantly higher recall rates than the line based text localization techniques, while also producing better-quality localization. We have also applied this technique on compressed images such as videos from wearable egocentric cameras. For the task of word spotting, we have introduced a novel mid-level word representation method. We have proposed a technique to create and exploit an intermediate representation of images based on text attributes which roughly correspond to character probability maps. Our representation extends the concept of Pyramidal Histogram Of Characters (PHOC) by exploiting Fully Convolutional Networks to derive a pixel-wise mapping of the character distribution within candidate word regions. We call this representation the Soft-PHOC. Furthermore, we show how to use Soft-PHOC descriptors for word spotting tasks through an efficient text line proposal algorithm. To evaluate the detected text, we propose a novel line based evaluation along with the classic bounding box based approach. We test our method on incidental scene text images which comprises real-life scenarios such as urban scenes. The importance of incidental scene text images is due to the complexity of backgrounds, perspective, variety of script and language, short text and little linguistic context. All of these factors together makes the incidental scene text images challenging.
	Address	November 2018
	Corporate Author				Thesis	Ph.D. thesis
	Publisher	Ediciones Graficas Rey	Place of Publication		Editor	Dimosthenis Karatzas;Andrew Bagdanov
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-84-948531-1-1	Medium
	Area		Expedition		Conference
	Notes	DAG; 600.121			Approved	no
	Call Number	Admin @ si @ Baz2018			Serial	3220
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	Author	David Aldavert
	Title	Efficient and Scalable Handwritten Word Spotting on Historical Documents using Bag of Visual Words			Type	Book Whole
	Year	2021	Publication	PhD Thesis, Universitat Autonoma de Barcelona-CVC	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Word spotting can be defined as the pattern recognition tasked aimed at locating and retrieving a specific keyword within a document image collection without explicitly transcribing the whole corpus. Its use is particularly interesting when applied in scenarios where Optical Character Recognition performs poorly or can not be used at all. This thesis focuses on such a scenario, word spotting on historical handwritten documents that have been written by a single author or by multiple authors with a similar calligraphy. This problem requires a visual signature that is robust to image artifacts, flexible to accommodate script variations and efficient to retrieve information in a rapid manner. For this, we have developed a set of word spotting methods that on their foundation use the well known Bag-of-Visual-Words (BoVW) representation. This representation has gained popularity among the document image analysis community to characterize handwritten words in an unsupervised manner. However, most approaches on this field rely on a basic BoVW configuration and disregard complex encoding and spatial representations. We determine which BoVW configurations provide the best performance boost to a spotting system. Then, we extend the segmentation-based word spotting, where word candidates are given a priori, to segmentation-free spotting. The proposed approach seeds the document images with overlapping word location candidates and characterizes them with a BoVW signature. Retrieval is achieved comparing the query and candidate signatures and returning the locations that provide a higher consensus. This is a simple but powerful approach that requires a more compact signature than in a segmentation-based scenario. We first project the BoVW signature into a reduced semantic topics space and then compress it further using Product Quantizers. The resulting signature only requires a few dozen bytes, allowing us to index thousands of pages on a common desktop computer. The final system still yields a performance comparable to the state-of-the-art despite all the information loss during the compression phases. Afterwards, we also study how to combine different modalities of information in order to create a query-by-X spotting system where, words are indexed using an information modality and queries are retrieved using another. We consider three different information modalities: visual, textual and audio. Our proposal is to create a latent feature space where features which are semantically related are projected onto the same topics. Creating thus a new feature space where information from different modalities can be compared. Later, we consider the codebook generation and descriptor encoding problem. The codebooks used to encode the BoVW signatures are usually created using an unsupervised clustering algorithm and, they require to test multiple parameters to determine which configuration is best for a certain document collection. We propose a semantic clustering algorithm which allows to estimate the best parameter from data. Since gather annotated data is costly, we use synthetically generated word images. The resulting codebook is database agnostic, i. e. a codebook that yields a good performance on document collections that use the same script. We also propose the use of an additional codebook to approximate descriptors and reduce the descriptor encoding complexity to sub-linear. Finally, we focus on the problem of signatures dimensionality. We propose a new symbol probability signature where each bin represents the probability that a certain symbol is present a certain location of the word image. This signature is extremely compact and combined with compression techniques can represent word images with just a few bytes per signature.
	Address	April 2021
	Corporate Author				Thesis	Ph.D. thesis
	Publisher	Ediciones Graficas Rey	Place of Publication		Editor	Marçal Rusiñol;Josep Llados
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-84-122714-5-4	Medium
	Area		Expedition		Conference
	Notes	DAG; 600.121			Approved	no
	Call Number	Admin @ si @ Ald2021			Serial	3601
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	Author	Alicia Fornes; Bart Lamiroy
	Title	Graphics Recognition, Current Trends and Evolutions			Type	Book Whole
	Year	2018	Publication	Graphics Recognition, Current Trends and Evolutions	Abbreviated Journal
	Volume	11009	Issue		Pages
	Keywords
	Abstract	This book constitutes the thoroughly refereed post-conference proceedings of the 12th International Workshop on Graphics Recognition, GREC 2017, held in Kyoto, Japan, in November 2017. The 10 revised full papers presented were carefully reviewed and selected from 14 initial submissions. They contain both classical and emerging topics of graphics rcognition, namely analysis and detection of diagrams, search and classification, optical music recognition, interpretation of engineering drawings and maps.
	Address
	Corporate Author				Thesis
	Publisher	Springer International Publishing	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-3-030-02283-9	Medium
	Area		Expedition		Conference
	Notes	DAG; 600.121			Approved	no
	Call Number	Admin @ si @ FoL2018			Serial	3171
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	Author	Suman Ghosh
	Title	Word Spotting and Recognition in Images from Heterogeneous Sources A			Type	Book Whole
	Year	2018	Publication	PhD Thesis, Universitat Autonoma de Barcelona-CVC	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Text is the most common way of information sharing from ages. With recent development of personal images databases and handwritten historic manuscripts the demand for algorithms to make these databases accessible for browsing and indexing are in rise. Enabling search or understanding large collection of manuscripts or image databases needs fast and robust methods. Researchers have found different ways to represent cropped words for understanding and matching, which works well when words are already segmented. However there is no trivial way to extend these for non-segmented documents. In this thesis we explore different methods for text retrieval and recognition from unsegmented document and scene images. Two different ways of representation exist in literature, one uses a fixed length representation learned from cropped words and another a sequence of features of variable length. Throughout this thesis, we have studied both these representation for their suitability in segmentation free understanding of text. In the first part we are focused on segmentation free word spotting using a fixed length representation. We extended the use of the successful PHOC (Pyramidal Histogram of Character) representation to segmentation free retrieval. In the second part of the thesis, we explore sequence based features and finally, we propose a unified solution where the same framework can generate both kind of representations.
	Address	November 2018
	Corporate Author				Thesis	Ph.D. thesis
	Publisher	Ediciones Graficas Rey	Place of Publication		Editor	Ernest Valveny
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-84-948531-0-4	Medium
	Area		Expedition		Conference
	Notes	DAG; 600.121			Approved	no
	Call Number	Admin @ si @ Gho2018			Serial	3217
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	Author	Pau Riba
	Title	Distilling Structure from Imagery: Graph-based Models for the Interpretation of Document Images			Type	Book Whole
	Year	2020	Publication	PhD Thesis, Universitat Autonoma de Barcelona-CVC	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	From its early stages, the community of Pattern Recognition and Computer Vision has considered the importance of leveraging the structural information when understanding images. Usually, graphs have been proposed as a suitable model to represent this kind of information due to their flexibility and representational power able to codify both, the components, objects, or entities and their pairwise relationship. Even though graphs have been successfully applied to a huge variety of tasks, as a result of their symbolic and relational nature, graphs have always suffered from some limitations compared to statistical approaches. Indeed, some trivial mathematical operations do not have an equivalence in the graph domain. For instance, in the core of many pattern recognition applications, there is a need to compare two objects. This operation, which is trivial when considering feature vectors defined in \(\mathbb{R}^n\), is not properly defined for graphs. In this thesis, we have investigated the importance of the structural information from two perspectives, the traditional graph-based methods and the new advances on Geometric Deep Learning. On the one hand, we explore the problem of defining a graph representation and how to deal with it on a large scale and noisy scenario. On the other hand, Graph Neural Networks are proposed to first redefine a Graph Edit Distance methodologies as a metric learning problem, and second, to apply them in a real use case scenario for the detection of repetitive patterns which define tables in invoice documents. As experimental framework, we have validated the different methodological contributions in the domain of Document Image Analysis and Recognition.
	Address
	Corporate Author				Thesis	Ph.D. thesis
	Publisher	Ediciones Graficas Rey	Place of Publication		Editor	Josep Llados;Alicia Fornes
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-84-121011-6-4	Medium
	Area		Expedition		Conference
	Notes	DAG; 600.121			Approved	no
	Call Number	Admin @ si @ Rib20			Serial	3478
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	Author	Raul Gomez
	Title	Exploiting the Interplay between Visual and Textual Data for Scene Interpretation			Type	Book Whole
	Year	2020	Publication	PhD Thesis, Universitat Autonoma de Barcelona-CVC	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Machine learning experimentation under controlled scenarios and standard datasets is necessary to compare algorithms performance by evaluating all of them in the same setup. However, experimentation on how those algorithms perform on unconstrained data and applied tasks to solve real world problems is also a must to ascertain how that research can contribute to our society. In this dissertation we experiment with the latest computer vision and natural language processing algorithms applying them to multimodal scene interpretation. Particularly, we research on how image and text understanding can be jointly exploited to address real world problems, focusing on learning from Social Media data. We address several tasks that involve image and textual information, discuss their characteristics and offer our experimentation conclusions. First, we work on detection of scene text in images. Then, we work with Social Media posts, exploiting the captions associated to images as supervision to learn visual features, which we apply to multimodal semantic image retrieval. Subsequently, we work with geolocated Social Media images with associated tags, experimenting on how to use the tags as supervision, on location sensitive image retrieval and on exploiting location information for image tagging. Finally, we work on a specific classification problem of Social Media publications consisting on an image and a text: Multimodal hate speech classification.
	Address
	Corporate Author				Thesis	Ph.D. thesis
	Publisher	Ediciones Graficas Rey	Place of Publication		Editor	Dimosthenis Karatzas;Lluis Gomez;Jaume Gibert
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-84-121011-7-1	Medium
	Area		Expedition		Conference
	Notes	DAG; 600.121			Approved	no
	Call Number	Admin @ si @ Gom20			Serial	3479
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