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Reference TypeConference Proceedings
Last Update2018:
Metadata Last Update2020: administrator
Citation KeyFreitasMira:2018:NeShDe
TitleNew Shape Descriptors based on Tensor Scale with Global Features
DateOct. 29 - Nov. 1, 2018
Access Date2020, Dec. 04
Number of Files1
Size2425 KiB
Context area
Author1 Freitas, Anderson M.
2 Miranda, Paulo A. V.
Affiliation1 Institute of Mathematics and Statistics, University of São Paulo
2 Institute of Mathematics and Statistics, University of São Paulo
EditorRoss, Arun
Gastal, Eduardo S. L.
Jorge, Joaquim A.
Queiroz, Ricardo L. de
Minetto, Rodrigo
Sarkar, Sudeep
Papa, João Paulo
Oliveira, Manuel M.
Arbeláez, Pablo
Mery, Domingo
Oliveira, Maria Cristina Ferreira de
Spina, Thiago Vallin
Mendes, Caroline Mazetto
Costa, Henrique Sérgio Gutierrez
Mejail, Marta Estela
Geus, Klaus de
Scheer, Sergio
Conference NameConference on Graphics, Patterns and Images, 31 (SIBGRAPI)
Conference LocationFoz do Iguaçu, PR, Brazil
Book TitleProceedings
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
History2018-10-25 19:19:49 :: -> administrator ::
2020-02-20 22:06:51 :: administrator -> :: 2018
Content and structure area
Is the master or a copy?is the master
Document Stagecompleted
Document Stagenot transferred
Tertiary TypeMaster's or Doctoral Work
KeywordsTensor Scale, Shape Descriptors, Content-Based Image Retrieval.
AbstractIn this work, two new shape descriptors are proposed for tasks in Content-Based Image Retrieval (CBIR) and Shape Analysis, which are built upon an extended tensor scale based on the Euclidean Distance Transform (EDT). First, the tensor scale algorithm is applied to extract shape attributes from its local structures as represented by the largest ellipse within a homogeneous region centered at each image pixel. In the new descriptors, the upper limit of the interval of local orientation of tensor scale ellipses is extended from π to 2π, to discriminate the description of local structures better. Then, the new descriptors are built based on different sampling approaches, aiming to summarize the most relevant features. Experimental results for different shape datasets (MPEG-7 and MNIST) are presented to illustrate and validate the methods. TSS can achieve high retrieval values comparable to state-of-the-art methods, which usually rely on time-consuming correspondence optimization algorithms, but uses a more straightforward and faster distance function, while the even faster linear complexity of TSB leads to a suitable solution for huge shape collections.
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Next Higher Units8JMKD3MGPAW/3RPADUS
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