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Author |
Jaume Gibert |
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Title |
Vector Space Embedding of Graphs via Statistics of Labelling Information |
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2012 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Pattern recognition is the task that aims at distinguishing objects among different classes. When such a task wants to be solved in an automatic way a crucial step is how to formally represent such patterns to the computer. Based on the different representational formalisms, we may distinguish between statistical and structural pattern recognition. The former describes objects as a set of measurements arranged in the form of what is called a feature vector. The latter assumes that relations between parts of the underlying objects need to be explicitly represented and thus it uses relational structures such as graphs for encoding their inherent information. Vector spaces are a very flexible mathematical structure that has allowed to come up with several efficient ways for the analysis of patterns under the form of feature vectors. Nevertheless, such a representation cannot explicitly cope with binary relations between parts of the objects and it is restricted to measure the exact same number of features for each pattern under study regardless of their complexity. Graph-based representations present the contrary situation. They can easily adapt to the inherent complexity of the patterns but introduce a problem of high computational complexity, hindering the design of efficient tools to process and analyse patterns.
Solving this paradox is the main goal of this thesis. The ideal situation for solving pattern recognition problems would be to represent the patterns using relational structures such as graphs, and to be able to use the wealthy repository of data processing tools from the statistical pattern recognition domain. An elegant solution to this problem is to transform the graph domain into a vector domain where any processing algorithm can be applied. In other words, by mapping each graph to a point in a vector space we automatically get access to the rich set of algorithms from the statistical domain to be applied in the graph domain. Such methodology is called graph embedding.
In this thesis we propose to associate feature vectors to graphs in a simple and very efficient way by just putting attention on the labelling information that graphs store. In particular, we count frequencies of node labels and of edges between labels. Although their locality, these features are able to robustly represent structurally global properties of graphs, when considered together in the form of a vector. We initially deal with the case of discrete attributed graphs, where features are easy to compute. The continuous case is tackled as a natural generalization of the discrete one, where rather than counting node and edge labelling instances, we count statistics of some representatives of them. We encounter how the proposed vectorial representations of graphs suffer from high dimensionality and correlation among components and we face these problems by feature selection algorithms. We also explore how the diversity of different embedding representations can be exploited in order to boost the performance of base classifiers in a multiple classifier systems framework. An extensive experimental evaluation finally shows how the methodology we propose can be efficiently computed and compete with other graph matching and embedding methodologies. |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Ernest Valveny |
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Admin @ si @ Gib2012 |
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2204 |
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Author |
Jaume Gibert |
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Title |
Learning structural representations and graph matching paradigms in the context of object recognition |
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2009 |
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CVC Technical Report |
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143 |
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Computer Vision Center |
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Master's thesis |
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Admin @ si @ Gib2009 |
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2397 |
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Author |
Farshad Nourbakhsh |
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Title |
Colour logo recognition |
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2009 |
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CVC Technical Report |
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145 |
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Computer Vision Center |
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Master's thesis |
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Bellaterra, Barcelona |
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Admin @ si @ Nou2009 |
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2399 |
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Author |
Nuria Cirera |
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Title |
Recognition of Handwritten Historical Documents |
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2012 |
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CVC Technical Report |
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174 |
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Admin @ si @ Cir2012 |
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2416 |
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Author |
Christophe Rigaud; Dimosthenis Karatzas; Jean-Christophe Burie; Jean-Marc Ogier |
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Title |
Speech balloon contour classification in comics |
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Conference Article |
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2013 |
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10th IAPR International Workshop on Graphics Recognition |
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Comic books digitization combined with subsequent comic book understanding create a variety of new applications, including mobile reading and data mining. Document understanding in this domain is challenging as comics are semi-structured documents, combining semantically important graphical and textual parts. In this work we detail a novel approach for classifying speech balloon in scanned comics book pages based on their contour time series. |
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Bethlehem; PA; USA; August 2013 |
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GREC |
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DAG; 600.056 |
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Admin @ si @ RKB2013 |
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2429 |
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Author |
Carles Sanchez; Oriol Ramos Terrades; Patricia Marquez; Enric Marti; Jaume Rocarias; Debora Gil |
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Title |
Evaluación automática de prácticas en Moodle para el aprendizaje autónomo en Ingenierías |
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Miscellaneous |
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2014 |
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8th International Congress on University Teaching and Innovation |
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Tarragona; juliol 2014 |
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CIDUI |
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IAM; 600.075;DAG |
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Admin @ si @ SRM2014 |
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2458 |
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Author |
Anjan Dutta |
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Title |
Inexact Subgraph Matching Applied to Symbol Spotting in Graphical Documents |
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Year |
2014 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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There is a resurgence in the use of structural approaches in the usual object recognition and retrieval problem. Graph theory, in particular, graph matching plays a relevant role in that. Specifically, the detection of an object (or a part of that) in an image in terms of structural features can be formulated as a subgraph matching. Subgraph matching is a challenging task. Specially due to the presence of outliers most of the graph matching algorithms do not perform well in subgraph matching scenario. Also exact subgraph isomorphism has proven to be an NP-complete problem. So naturally, in graph matching community, there are lot of efforts addressing the problem of subgraph matching within suboptimal bound. Most of them work with approximate algorithms that try to get an inexact solution in estimated way. In addition, usual recognition must cope with distortion. Inexact graph matching consists in finding the best isomorphism under a similarity measure. Theoretically this thesis proposes algorithms for solving subgraph matching in an approximate and inexact way.
We consider the symbol spotting problem on graphical documents or line drawings from application point of view. This is a well known problem in the graphics recognition community. It can be further applied for indexing and classification of documents based on their contents. The structural nature of this kind of documents easily motivates one for giving a graph based representation. So the symbol spotting problem on graphical documents can be considered as a subgraph matching problem. The main challenges in this application domain is the noise and distortions that might come during the usage, digitalization and raster to vector conversion of those documents. Apart from that computer vision nowadays is not any more confined within a limited number of images. So dealing a huge number of images with graph based method is a further challenge.
In this thesis, on one hand, we have worked on efficient and robust graph representation to cope with the noise and distortions coming from documents. On the other hand, we have worked on different graph based methods and framework to solve the subgraph matching problem in a better approximated way, which can also deal with considerable number of images. Firstly, we propose a symbol spotting method by hashing serialized subgraphs. Graph serialization allows to create factorized substructures such as graph paths, which can be organized in hash tables depending on the structural similarities of the serialized subgraphs. The involvement of hashing techniques helps to reduce the search space substantially and speeds up the spotting procedure. Secondly, we introduce contextual similarities based on the walk based propagation on tensor product graph. These contextual similarities involve higher order information and more reliable than pairwise similarities. We use these higher order similarities to formulate subgraph matching as a node and edge selection problem in the tensor product graph. Thirdly, we propose near convex grouping to form near convex region adjacency graph which eliminates the limitations of traditional region adjacency graph representation for graphic recognition. Fourthly, we propose a hierarchical graph representation by simplifying/correcting the structural errors to create a hierarchical graph of the base graph. Later these hierarchical graph structures are matched with some graph matching methods. Apart from that, in this thesis we have provided an overall experimental comparison of all the methods and some of the state-of-the-art methods. Furthermore, some dataset models have also been proposed. |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Josep Llados;Umapada Pal |
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978-84-940902-4-0 |
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DAG; 600.077 |
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Admin @ si @ Dut2014 |
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2465 |
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Author |
Clement Guerin; Christophe Rigaud; Karell Bertet; Jean-Christophe Burie; Arnaud Revel ; Jean-Marc Ogier |
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Title |
Réduction de l’espace de recherche pour les personnages de bandes dessinées |
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2014 |
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19th National Congress Reconnaissance de Formes et l'Intelligence Artificielle |
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contextual search; document analysis; comics characters |
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Les bandes dessinées représentent un patrimoine culturel important dans de nombreux pays et leur numérisation massive offre la possibilité d'effectuer des recherches dans le contenu des images. À ce jour, ce sont principalement les structures des pages et leurs contenus textuels qui ont été étudiés, peu de travaux portent sur le contenu graphique. Nous proposons de nous appuyer sur des éléments déjà étudiés tels que la position des cases et des bulles, pour réduire l'espace de recherche et localiser les personnages en fonction de la queue des bulles. L'évaluation de nos différentes contributions à partir de la base eBDtheque montre un taux de détection des queues de bulle de 81.2%, de localisation des personnages allant jusqu'à 85% et un gain d'espace de recherche de plus de 50%. |
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Rouen; Francia; July 2014 |
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RFIA |
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DAG; 600.077 |
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Admin @ si @ GRB2014 |
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2480 |
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Author |
Christophe Rigaud; Clement Guerin |
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Title |
Localisation contextuelle des personnages de bandes dessinées |
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2014 |
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Colloque International Francophone sur l'Écrit et le Document |
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Les auteurs proposent une méthode de localisation des personnages dans des cases de bandes dessinées en s'appuyant sur les caractéristiques des bulles de dialogue. L'évaluation montre un taux de localisation des personnages allant jusqu'à 65%. |
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Nancy; Francia; March 2014 |
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CIFED |
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DAG; 600.077 |
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Admin @ si @ RiG2014 |
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2481 |
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Francisco Alvaro; Francisco Cruz; Joan Andreu Sanchez; Oriol Ramos Terrades; Jose Miguel Benedi |
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Title |
Structure Detection and Segmentation of Documents Using 2D Stochastic Context-Free Grammars |
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2015 |
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Neurocomputing |
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NEUCOM |
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150 |
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A |
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147-154 |
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document image analysis; stochastic context-free grammars; text classi cation features |
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In this paper we de ne a bidimensional extension of Stochastic Context-Free Grammars for structure detection and segmentation of images of documents.
Two sets of text classi cation features are used to perform an initial classi cation of each zone of the page. Then, the document segmentation is obtained as the most likely hypothesis according to a stochastic grammar. We used a dataset of historical marriage license books to validate this approach. We also tested several inference algorithms for Probabilistic Graphical Models
and the results showed that the proposed grammatical model outperformed
the other methods. Furthermore, grammars also provide the document structure
along with its segmentation. |
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DAG; 601.158; 600.077; 600.061 |
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Admin @ si @ ACS2015 |
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2531 |
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