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Author |
Jaume Gibert; Ernest Valveny; Horst Bunke |
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Title |
Graph Embedding in Vector Spaces by Node Attribute Statistics |
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Journal Article |
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Year |
2012 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
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Volume |
45 |
Issue |
9 |
Pages |
3072-3083 |
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Keywords |
Structural pattern recognition; Graph embedding; Data clustering; Graph classification |
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Abstract |
Graph-based representations are of broad use and applicability in pattern recognition. They exhibit, however, a major drawback with regards to the processing tools that are available in their domain. Graphembedding into vectorspaces is a growing field among the structural pattern recognition community which aims at providing a feature vector representation for every graph, and thus enables classical statistical learning machinery to be used on graph-based input patterns. In this work, we propose a novel embedding methodology for graphs with continuous nodeattributes and unattributed edges. The approach presented in this paper is based on statistics of the node labels and the edges between them, based on their similarity to a set of representatives. We specifically deal with an important issue of this methodology, namely, the selection of a suitable set of representatives. In an experimental evaluation, we empirically show the advantages of this novel approach in the context of different classification problems using several databases of graphs. |
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0031-3203 |
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DAG |
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no |
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Admin @ si @ GVB2012a |
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1992 |
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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 |
Issue |
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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Symbol spotting; Graphics recognition; Graph matching; Graph serialization; Graph factorization; Graph paths; Hashing |
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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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Admin @ si @ DLP2012 |
Serial |
2127 |
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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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Volume |
45 |
Issue |
5 |
Pages |
1972-1983 |
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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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no |
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Admin @ si @ RPL2012a |
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2133 |
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Author |
Albert Gordo; Florent Perronnin; Yunchao Gong; Svetlana Lazebnik |
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Title |
Asymmetric Distances for Binary Embeddings |
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Journal Article |
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Year |
2014 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
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Volume |
36 |
Issue |
1 |
Pages |
33-47 |
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Abstract |
In large-scale query-by-example retrieval, embedding image signatures in a binary space offers two benefits: data compression and search efficiency. While most embedding algorithms binarize both query and database signatures, it has been noted that this is not strictly a requirement. Indeed, asymmetric schemes which binarize the database signatures but not the query still enjoy the same two benefits but may provide superior accuracy. In this work, we propose two general asymmetric distances which are applicable to a wide variety of embedding techniques including Locality Sensitive Hashing (LSH), Locality Sensitive Binary Codes (LSBC), Spectral Hashing (SH), PCA Embedding (PCAE), PCA Embedding with random rotations (PCAE-RR), and PCA Embedding with iterative quantization (PCAE-ITQ). We experiment on four public benchmarks containing up to 1M images and show that the proposed asymmetric distances consistently lead to large improvements over the symmetric Hamming distance for all binary embedding techniques. |
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0162-8828 |
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DAG; 600.045; 605.203; 600.077 |
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no |
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Admin @ si @ GPG2014 |
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2272 |
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Author |
Oriol Ramos Terrades; Ernest Valveny; Salvatore Tabbone |
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Title |
Optimal Classifier Fusion in a Non-Bayesian Probabilistic Framework |
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Journal Article |
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Year |
2009 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
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Volume |
31 |
Issue |
9 |
Pages |
1630–1644 |
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The combination of the output of classifiers has been one of the strategies used to improve classification rates in general purpose classification systems. Some of the most common approaches can be explained using the Bayes' formula. In this paper, we tackle the problem of the combination of classifiers using a non-Bayesian probabilistic framework. This approach permits us to derive two linear combination rules that minimize misclassification rates under some constraints on the distribution of classifiers. In order to show the validity of this approach we have compared it with other popular combination rules from a theoretical viewpoint using a synthetic data set, and experimentally using two standard databases: the MNIST handwritten digit database and the GREC symbol database. Results on the synthetic data set show the validity of the theoretical approach. Indeed, results on real data show that the proposed methods outperform other common combination schemes. |
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0162-8828 |
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DAG |
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DAG @ dag @ RVT2009 |
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1220 |
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Author |
Yunchao Gong; Svetlana Lazebnik; Albert Gordo; Florent Perronnin |
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Title |
Iterative quantization: A procrustean approach to learning binary codes for Large-Scale Image Retrieval |
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Journal Article |
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2012 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence |
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TPAMI |
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35 |
Issue |
12 |
Pages |
2916-2929 |
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This paper addresses the problem of learning similarity-preserving binary codes for efficient similarity search in large-scale image collections. We formulate this problem in terms of finding a rotation of zero-centered data so as to minimize the quantization error of mapping this data to the vertices of a zero-centered binary hypercube, and propose a simple and efficient alternating minimization algorithm to accomplish this task. This algorithm, dubbed iterative quantization (ITQ), has connections to multi-class spectral clustering and to the orthogonal Procrustes problem, and it can be used both with unsupervised data embeddings such as PCA and supervised embeddings such as canonical correlation analysis (CCA). The resulting binary codes significantly outperform several other state-of-the-art methods. We also show that further performance improvements can result from transforming the data with a nonlinear kernel mapping prior to PCA or CCA. Finally, we demonstrate an application of ITQ to learning binary attributes or “classemes” on the ImageNet dataset. |
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0162-8828 |
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978-1-4577-0394-2 |
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DAG |
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Admin @ si @ GLG 2012b |
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2008 |
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Author |
Jon Almazan; Albert Gordo; Alicia Fornes; Ernest Valveny |
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Title |
Word Spotting and Recognition with Embedded Attributes |
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2014 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence |
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TPAMI |
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36 |
Issue |
12 |
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2552 - 2566 |
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This article addresses the problems of word spotting and word recognition on images. In word spotting, the goal is to find all instances of a query word in a dataset of images. In recognition, the goal is to recognize the content of the word image, usually aided by a dictionary or lexicon. We describe an approach in which both word images and text strings are embedded in a common vectorial subspace. This is achieved by a combination of label embedding and attributes learning, and a common subspace regression. In this subspace, images and strings that represent the same word are close together, allowing one to cast recognition and retrieval tasks as a nearest neighbor problem. Contrary to most other existing methods, our representation has a fixed length, is low dimensional, and is very fast to compute and, especially, to compare. We test our approach on four public datasets of both handwritten documents and natural images showing results comparable or better than the state-of-the-art on spotting and recognition tasks. |
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0162-8828 |
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DAG; 600.056; 600.045; 600.061; 602.006; 600.077 |
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Admin @ si @ AGF2014a |
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2483 |
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Author |
Miquel Ferrer; Ernest Valveny; F. Serratosa |
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Title |
Median graph: A new exact algorithm using a distance based on the maximum common subgraph |
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Year |
2009 |
Publication |
Pattern Recognition Letters |
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PRL |
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30 |
Issue |
5 |
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579–588 |
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Median graphs have been presented as a useful tool for capturing the essential information of a set of graphs. Nevertheless, computation of optimal solutions is a very hard problem. In this work we present a new and more efficient optimal algorithm for the median graph computation. With the use of a particular cost function that permits the definition of the graph edit distance in terms of the maximum common subgraph, and a prediction function in the backtracking algorithm, we reduce the size of the search space, avoiding the evaluation of a great amount of states and still obtaining the exact median. We present a set of experiments comparing our new algorithm against the previous existing exact algorithm using synthetic data. In addition, we present the first application of the exact median graph computation to real data and we compare the results against an approximate algorithm based on genetic search. These experimental results show that our algorithm outperforms the previous existing exact algorithm and in addition show the potential applicability of the exact solutions to real problems. |
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Elsevier Science Inc. |
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0167-8655 |
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DAG |
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DAG @ dag @ FVS2009a |
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1114 |
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Author |
Ernest Valveny; Enric Marti |
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A model for image generation and symbol recognition through the deformation of lineal shapes |
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2003 |
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Pattern Recognition Letters |
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PRL |
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24 |
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15 |
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2857-2867 |
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We describe a general framework for the recognition of distorted images of lineal shapes, which relies on three items: a model to represent lineal shapes and their deformations, a model for the generation of distorted binary images and the combination of both models in a common probabilistic framework, where the generation of deformations is related to an internal energy, and the generation of binary images to an external energy. Then, recognition consists in the minimization of a global energy function, performed by using the EM algorithm. This general framework has been applied to the recognition of hand-drawn lineal symbols in graphic documents. |
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Elsevier Science Inc. |
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New York, NY, USA |
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0167-8655 |
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DAG; IAM |
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IAM @ iam @ VAM2003 |
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1653 |
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