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Author
Josep Llados; Horst Bunke; Enric Marti
Title
Finding rotational symmetries by cyclic string matching
Type
Journal Article
Year
1997
Publication
Pattern recognition letters
Abbreviated Journal
PRL
Volume
18
Issue
14
Pages
1435-1442
Keywords
Rotational symmetry; Reflectional symmetry; String matching
Abstract
Symmetry is an important shape feature. In this paper, a simple and fast method to detect perfect and distorted rotational symmetries of 2D objects is described. The boundary of a shape is polygonally approximated and represented as a string. Rotational symmetries are found by cyclic string matching between two identical copies of the shape string. The set of minimum cost edit sequences that transform the shape string to a cyclically shifted version of itself define the rotational symmetry and its order. Finally, a modification of the algorithm is proposed to detect reflectional symmetries. Some experimental results are presented to show the reliability of the proposed algorithm
Address
Corporate Author
Thesis
Publisher
Elsevier
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
DAG;IAM;
Approved
no
Call Number
IAM @ iam @ LBM1997a
Serial
1562
Permanent link to this record
Author
Jose Antonio Rodriguez; Florent Perronnin; Gemma Sanchez; Josep Llados
Title
Unsupervised writer adaptation of whole-word HMMs with application to word-spotting
Type
Journal Article
Year
2010
Publication
Pattern Recognition Letters
Abbreviated Journal
PRL
Volume
31
Issue
8
Pages
742–749
Keywords
Word-spotting; Handwriting recognition; Writer adaptation; Hidden Markov model; Document analysis
Abstract
In this paper we propose a novel approach for writer adaptation in a handwritten word-spotting task. The method exploits the fact that the semi-continuous hidden Markov model separates the word model parameters into (i) a codebook of shapes and (ii) a set of word-specific parameters.
Our main contribution is to employ this property to derive writer-specific word models by statistically adapting an initial universal codebook to each document. This process is unsupervised and does not even require the appearance of the keyword(s) in the searched document. Experimental results show an increase in performance when this adaptation technique is applied. To the best of our knowledge, this is the first work dealing with adaptation for word-spotting. The preliminary version of this paper obtained an IBM Best Student Paper Award at the 19th International Conference on Pattern Recognition.
Address
Corporate Author
Thesis
Publisher
Elsevier
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
DAG
Approved
no
Call Number
DAG @ dag @ RPS2010
Serial
1290
Permanent link to this record
Author
Albert Gordo; Florent Perronnin; Ernest Valveny
Title
Large-scale document image retrieval and classification with runlength histograms and binary embeddings
Type
Journal Article
Year
2013
Publication
Pattern Recognition
Abbreviated Journal
PR
Volume
46
Issue
7
Pages
1898-1905
Keywords
visual document descriptor; compression; large-scale; retrieval; classification
Abstract
We present a new document image descriptor based on multi-scale runlength
histograms. This descriptor does not rely on layout analysis and can be
computed efficiently. We show how this descriptor can achieve state-of-theart
results on two very different public datasets in classification and retrieval
tasks. Moreover, we show how we can compress and binarize these descriptors
to make them suitable for large-scale applications. We can achieve state-ofthe-
art results in classification using binary descriptors of as few as 16 to 64
bits.
Address
Corporate Author
Thesis
Publisher
Elsevier
Place of Publication
Editor
Language
Summary Language
Original Title
Series Editor
Series Title
Abbreviated Series Title
Series Volume
Series Issue
Edition
ISSN
0031-3203
ISBN
Medium
Area
Expedition
Conference
Notes
DAG; 600.042; 600.045; 605.203
Approved
no
Call Number
Admin @ si @ GPV2013
Serial
2306
Permanent link to this record
Author
Anjan Dutta; Josep Llados; Umapada Pal
Title
A symbol spotting approach in graphical documents by hashing serialized graphs
Type
Journal Article
Year
2013
Publication
Pattern Recognition
Abbreviated Journal
PR
Volume
46
Issue
3
Pages
752-768
Keywords
Symbol spotting; Graphics recognition; Graph matching; Graph serialization; Graph factorization; Graph paths; Hashing
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.
Address
Corporate Author
Thesis
Publisher
Elsevier
Place of Publication
Editor
Language
Summary Language
Original Title
Series Editor
Series Title
Abbreviated Series Title
Series Volume
Series Issue
Edition
ISSN
0031-3203
ISBN
Medium
Area
Expedition
Conference
Notes
DAG; 600.042; 600.045; 605.203; 601.152
Approved
no
Call Number
Admin @ si @ DLP2012
Serial
2127
Permanent link to this record
Author
Muhammad Muzzamil Luqman; Jean-Yves Ramel; Josep Llados; Thierry Brouard
Title
Fuzzy Multilevel Graph Embedding
Type
Journal Article
Year
2013
Publication
Pattern Recognition
Abbreviated Journal
PR
Volume
46
Issue
2
Pages
551-565
Keywords
Pattern recognition; Graphics recognition; Graph clustering; Graph classification; Explicit graph embedding; Fuzzy logic
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.
Address
Corporate Author
Thesis
Publisher
Elsevier
Place of Publication
Editor
Language
Summary Language
Original Title
Series Editor
Series Title
Abbreviated Series Title
Series Volume
Series Issue
Edition
ISSN
0031-3203
ISBN
Medium
Area
Expedition
Conference
Notes
DAG; 600.042; 600.045; 605.203
Approved
no
Call Number
Admin @ si @ LRL2013a
Serial
2270
Permanent link to this record
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