14 research outputs found
Report on the Second Symbol Recognition Contest
http://www.springer.com/lncsFollowing the experience of the first edition of the international symbol recognition contest held during GREC'03 in Barcelona, a second edition has been organized during GREC'05. In this paper, first, we bring to mind the general principles of both contests before presenting more specifically the details of this last edition. In particular, we describe the dataset used in the contest, the methods that took part in it, and the analysis of the results obtained by the participants. We conclude with a synthesis of the contributions and lacks of these two editions, and some leads for the organization of a forthcoming contest
Graphic Symbol Recognition using Graph Based Signature and Bayesian Network Classifier
We present a new approach for recognition of complex graphic symbols in
technical documents. Graphic symbol recognition is a well known challenge in
the field of document image analysis and is at heart of most graphic
recognition systems. Our method uses structural approach for symbol
representation and statistical classifier for symbol recognition. In our system
we represent symbols by their graph based signatures: a graphic symbol is
vectorized and is converted to an attributed relational graph, which is used
for computing a feature vector for the symbol. This signature corresponds to
geometry and topology of the symbol. We learn a Bayesian network to encode
joint probability distribution of symbol signatures and use it in a supervised
learning scenario for graphic symbol recognition. We have evaluated our method
on synthetically deformed and degraded images of pre-segmented 2D architectural
and electronic symbols from GREC databases and have obtained encouraging
recognition rates.Comment: 5 pages, 8 figures, Tenth International Conference on Document
Analysis and Recognition (ICDAR), IEEE Computer Society, 2009, volume 10,
1325-132
Employing fuzzy intervals and loop-based methodology for designing structural signature: an application to symbol recognition
Motivation of our work is to present a new methodology for symbol
recognition. We support structural methods for representing visual associations
in graphic documents. The proposed method employs a structural approach for
symbol representation and a statistical classifier for recognition. We
vectorize a graphic symbol, encode its topological and geometrical information
by an ARG and compute a signature from this structural graph. To address the
sensitivity of structural representations to deformations and degradations, we
use data adapted fuzzy intervals while computing structural signature. The
joint probability distribution of signatures is encoded by a Bayesian network.
This network in fact serves as a mechanism for pruning irrelevant features and
choosing a subset of interesting features from structural signatures, for
underlying symbol set. Finally we deploy the Bayesian network in supervised
learning scenario for recognizing query symbols. We have evaluated the
robustness of our method against noise, on synthetically deformed and degraded
images of pre-segmented 2D architectural and electronic symbols from GREC
databases and have obtained encouraging recognition rates. A second set of
experimentation was carried out for evaluating the performance of our method
against context noise i.e. symbols cropped from complete documents. The results
support the use of our signature by a symbol spotting system.Comment: 10 pages, Eighth IAPR International Workshop on Graphics RECognition
(GREC), 2009, volume 8, 22-3
Fuzzy Intervals for Designing Structural Signature: An Application to Graphic Symbol Recognition
Revised selected papers from Eighth IAPR International Workshop on Graphics RECognition (GREC) 2009.The motivation behind our work is to present a new methodology for symbol recognition. The proposed method employs a structural approach for representing visual associations in symbols and a statistical classifier for recognition. We vectorize a graphic symbol, encode its topological and geometrical information by an attributed relational graph and compute a signature from this structural graph. We have addressed the sensitivity of structural representations to noise, by using data adapted fuzzy intervals. The joint probability distribution of signatures is encoded by a Bayesian network, which serves as a mechanism for pruning irrelevant features and choosing a subset of interesting features from structural signatures of underlying symbol set. The Bayesian network is deployed in a supervised learning scenario for recognizing query symbols. The method has been evaluated for robustness against degradations & deformations on pre-segmented 2D linear architectural & electronic symbols from GREC databases, and for its recognition abilities on symbols with context noise i.e. cropped symbols
Discriminative prototype selection methods for graph embedding
Graphs possess a strong representational power for many types of patterns. However, a main limitation in their use for pattern analysis derives from their difficult mathematical treatment. One way of circumventing this problem is that of transforming the graphs into a vector space by means of graph embedding. Such an embedding can be conveniently obtained by using a set of prototype graphs and a dissimilarity measure. However, when we apply this approach to a set of class-labelled graphs, it is challenging to select prototypes capturing both the salient structure within each class and inter-class separation. In this paper, we introduce a novel framework for selecting a set of prototypes from a labelled graph set taking their discriminative power into account. Experimental results showed that such a discriminative prototype selection framework can achieve superior results in classification compared to other well-established prototype selection approaches. © 2012 Elsevier Ltd
A hypergraph-based model for graph clustering: application to image indexing
Version finale disponible : www.springerlink.comInternational audienceIn this paper, we introduce a prototype-based clustering algorithm dealing with graphs. We propose a hypergraph-based model for graph data sets by allowing clusters overlapping. More precisely, in this representation one graph can be assigned to more than one cluster. Using the concept of the graph median and a given threshold, the proposed algorithm detects automatically the number of classes in the graph database. We consider clusters as hyperedges in our hypergraph model and we define a retrieval technique indexing the database with hyperedge centroids. This model is interesting to travel the data set and efficient to cluster and retrieve graphs
Design of Evolutionary Methods Applied to the Learning of Bayesian Network Structures
Bayesian Network, Ahmed Rebai (Ed.), ISBN: 978-953-307-124-4, pp. 13-38