DocumentCode :
153355
Title :
Spotting Symbol Using Sparsity over Learned Dictionary of Local Descriptors
Author :
Thanh-Ha Do ; Tabbone, Salvatore ; Ramos Terrades, Oriol
Author_Institution :
LORIA, Univ. de Lorraine, Vandoeuvre-les-Nancy, France
fYear :
2014
fDate :
7-10 April 2014
Firstpage :
156
Lastpage :
160
Abstract :
This paper proposes a new approach to spot symbols into graphical documents using sparse representations. More specifically, a dictionary is learned from a training database of local descriptors defined over the documents. Following their sparse representations, interest points sharing similar properties are used to define interest regions. Using an original adaptation of information retrieval techniques, a vector model for interest regions and for a query symbol is built based on its sparsity in a visual vocabulary where the visual words are columns in the learned dictionary. The matching process is performed comparing the similarity between vector models. Evaluation on SESYD datasets demonstrates that our method is promising.
Keywords :
dictionaries; document image processing; image matching; image representation; query processing; vectors; SESYD datasets; graphical documents; information retrieval techniques; interest points; interest regions; local descriptors; matching process; query symbol; sparse representations; symbol spotting; training database; vector models similarity; visual vocabulary; visual words; Computational modeling; Dictionaries; Indexing; Matching pursuit algorithms; Vectors; Visualization; Vocabulary;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Document Analysis Systems (DAS), 2014 11th IAPR International Workshop on
Conference_Location :
Tours
Print_ISBN :
978-1-4799-3243-6
Type :
conf
DOI :
10.1109/DAS.2014.62
Filename :
6830989
Link To Document :
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