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Ernest Valveny; Robert Benavente; Agata Lapedriza; Miquel Ferrer; Jaume Garcia; Gemma Sanchez |
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Title |
Adaptation of a computer programming course to the EXHE requirements: evaluation five years later |
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Miscellaneous |
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2012 |
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European Journal of Engineering Education |
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37 |
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3 |
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243-254 |
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DAG; CIC; OR; invisible;MV |
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Admin @ si @ VBL2012 |
Serial |
2070 |
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Author |
Muhammad Muzzamil Luqman; Jean-Yves Ramel; Josep Llados; Thierry Brouard |
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Title |
Fuzzy Multilevel Graph Embedding |
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Journal Article |
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Year |
2013 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
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Volume |
46 |
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2 |
Pages |
551-565 |
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Keywords |
Pattern recognition; Graphics recognition; Graph clustering; Graph classification; Explicit graph embedding; Fuzzy logic |
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Abstract |
Structural pattern recognition approaches offer the most expressive, convenient, powerful but computational expensive representations of underlying relational information. To benefit from mature, less expensive and efficient state-of-the-art machine learning models of statistical pattern recognition they must be mapped to a low-dimensional vector space. Our method of explicit graph embedding bridges the gap between structural and statistical pattern recognition. We extract the topological, structural and attribute information from a graph and encode numeric details by fuzzy histograms and symbolic details by crisp histograms. The histograms are concatenated to achieve a simple and straightforward embedding of graph into a low-dimensional numeric feature vector. Experimentation on standard public graph datasets shows that our method outperforms the state-of-the-art methods of graph embedding for richly attributed graphs. |
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Elsevier |
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0031-3203 |
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DAG; 600.042; 600.045; 605.203 |
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Admin @ si @ LRL2013a |
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2270 |
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Author |
Anjan Dutta; Josep Llados; Umapada Pal |
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Title |
A symbol spotting approach in graphical documents by hashing serialized graphs |
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Journal Article |
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Year |
2013 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
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Volume |
46 |
Issue |
3 |
Pages |
752-768 |
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Keywords |
Symbol spotting; Graphics recognition; Graph matching; Graph serialization; Graph factorization; Graph paths; Hashing |
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Abstract |
In this paper we propose a symbol spotting technique in graphical documents. Graphs are used to represent the documents and a (sub)graph matching technique is used to detect the symbols in them. We propose a graph serialization to reduce the usual computational complexity of graph matching. Serialization of graphs is performed by computing acyclic graph paths between each pair of connected nodes. Graph paths are one-dimensional structures of graphs which are less expensive in terms of computation. At the same time they enable robust localization even in the presence of noise and distortion. Indexing in large graph databases involves a computational burden as well. We propose a graph factorization approach to tackle this problem. Factorization is intended to create a unified indexed structure over the database of graphical documents. Once graph paths are extracted, the entire database of graphical documents is indexed in hash tables by locality sensitive hashing (LSH) of shape descriptors of the paths. The hashing data structure aims to execute an approximate k-NN search in a sub-linear time. We have performed detailed experiments with various datasets of line drawings and compared our method with the state-of-the-art works. The results demonstrate the effectiveness and efficiency of our technique. |
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Elsevier |
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0031-3203 |
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DAG; 600.042; 600.045; 605.203; 601.152 |
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Call Number |
Admin @ si @ DLP2012 |
Serial |
2127 |
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Author |
Josep Llados; Marçal Rusiñol; Alicia Fornes; David Fernandez; Anjan Dutta |
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Title |
On the Influence of Word Representations for Handwritten Word Spotting in Historical Documents |
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Journal Article |
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Year |
2012 |
Publication |
International Journal of Pattern Recognition and Artificial Intelligence |
Abbreviated Journal |
IJPRAI |
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Volume |
26 |
Issue |
5 |
Pages |
1263002-126027 |
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Keywords |
Handwriting recognition; word spotting; historical documents; feature representation; shape descriptors Read More: http://www.worldscientific.com/doi/abs/10.1142/S0218001412630025 |
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Abstract |
0,624 JCR
Word spotting is the process of retrieving all instances of a queried keyword from a digital library of document images. In this paper we evaluate the performance of different word descriptors to assess the advantages and disadvantages of statistical and structural models in a framework of query-by-example word spotting in historical documents. We compare four word representation models, namely sequence alignment using DTW as a baseline reference, a bag of visual words approach as statistical model, a pseudo-structural model based on a Loci features representation, and a structural approach where words are represented by graphs. The four approaches have been tested with two collections of historical data: the George Washington database and the marriage records from the Barcelona Cathedral. We experimentally demonstrate that statistical representations generally give a better performance, however it cannot be neglected that large descriptors are difficult to be implemented in a retrieval scenario where word spotting requires the indexation of data with million word images. |
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DAG |
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no |
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Call Number |
Admin @ si @ LRF2012 |
Serial |
2128 |
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Author |
Alicia Fornes; Anjan Dutta; Albert Gordo; Josep Llados |
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Title |
CVC-MUSCIMA: A Ground-Truth of Handwritten Music Score Images for Writer Identification and Staff Removal |
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Journal Article |
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Year |
2012 |
Publication |
International Journal on Document Analysis and Recognition |
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IJDAR |
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15 |
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3 |
Pages |
243-251 |
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Music scores; Handwritten documents; Writer identification; Staff removal; Performance evaluation; Graphics recognition; Ground truths |
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Abstract |
0,405JCR
The analysis of music scores has been an active research field in the last decades. However, there are no publicly available databases of handwritten music scores for the research community. In this paper we present the CVC-MUSCIMA database and ground-truth of handwritten music score images. The dataset consists of 1,000 music sheets written by 50 different musicians. It has been especially designed for writer identification and staff removal tasks. In addition to the description of the dataset, ground-truth, partitioning and evaluation metrics, we also provide some base-line results for easing the comparison between different approaches. |
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1433-2833 |
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DAG |
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no |
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Call Number |
Admin @ si @ FDG2012 |
Serial |
2129 |
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Author |
Partha Pratim Roy; Umapada Pal; Josep Llados; Mathieu Nicolas Delalandre |
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Title |
Multi-oriented touching text character segmentation in graphical documents using dynamic programming |
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Journal Article |
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Year |
2012 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
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45 |
Issue |
5 |
Pages |
1972-1983 |
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Abstract |
2,292 JCR
The touching character segmentation problem becomes complex when touching strings are multi-oriented. Moreover in graphical documents sometimes characters in a single-touching string have different orientations. Segmentation of such complex touching is more challenging. In this paper, we present a scheme towards the segmentation of English multi-oriented touching strings into individual characters. When two or more characters touch, they generate a big cavity region in the background portion. Based on the convex hull information, at first, we use this background information to find some initial points for segmentation of a touching string into possible primitives (a primitive consists of a single character or part of a character). Next, the primitives are merged to get optimum segmentation. A dynamic programming algorithm is applied for this purpose using the total likelihood of characters as the objective function. A SVM classifier is used to find the likelihood of a character. To consider multi-oriented touching strings the features used in the SVM are invariant to character orientation. Experiments were performed in different databases of real and synthetic touching characters and the results show that the method is efficient in segmenting touching characters of arbitrary orientations and sizes. |
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0031-3203 |
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DAG |
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Admin @ si @ RPL2012a |
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2133 |
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Author |
Partha Pratim Roy; Umapada Pal; Josep Llados |
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Title |
Text line extraction in graphical documents using background and foreground |
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Journal Article |
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Year |
2012 |
Publication |
International Journal on Document Analysis and Recognition |
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IJDAR |
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15 |
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3 |
Pages |
227-241 |
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0,405 JCR
In graphical documents (e.g., maps, engineering drawings), artistic documents etc., the text lines are annotated in multiple orientations or curvilinear way to illustrate different locations or symbols. For the optical character recognition of such documents, individual text lines from the documents need to be extracted. In this paper, we propose a novel method to segment such text lines and the method is based on the foreground and background information of the text components. To effectively utilize the background information, a water reservoir concept is used here. In the proposed scheme, at first, individual components are detected and grouped into character clusters in a hierarchical way using size and positional information. Next, the clusters are extended in two extreme sides to determine potential candidate regions. Finally, with the help of these candidate regions,
individual lines are extracted. The experimental results are presented on different datasets of graphical documents, camera-based warped documents, noisy images containing seals, etc. The results demonstrate that our approach is robust and invariant to size and orientation of the text lines present in
the document. |
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1433-2833 |
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DAG |
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Admin @ si @ RPL2012b |
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2134 |
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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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Book Whole |
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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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DAG |
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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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Report |
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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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DAG |
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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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Report |
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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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