@InProceedings{FreitasMira:2018:NeShDe,
author = "Freitas, Anderson M. and Miranda, Paulo A. V.",
affiliation = "Institute of Mathematics and Statistics, University of S{\~a}o
Paulo and Institute of Mathematics and Statistics, University of
S{\~a}o Paulo",
title = "New Shape Descriptors based on Tensor Scale with Global Features",
booktitle = "Proceedings...",
year = "2018",
editor = "Ross, Arun and Gastal, Eduardo S. L. and Jorge, Joaquim A. and
Queiroz, Ricardo L. de and Minetto, Rodrigo and Sarkar, Sudeep and
Papa, Jo{\~a}o Paulo and Oliveira, Manuel M. and Arbel{\'a}ez,
Pablo and Mery, Domingo and Oliveira, Maria Cristina Ferreira de
and Spina, Thiago Vallin and Mendes, Caroline Mazetto and Costa,
Henrique S{\'e}rgio Gutierrez and Mejail, Marta Estela and Geus,
Klaus de and Scheer, Sergio",
organization = "Conference on Graphics, Patterns and Images, 31. (SIBGRAPI)",
publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
address = "Porto Alegre",
keywords = "Tensor Scale, Shape Descriptors, Content-Based Image Retrieval.",
abstract = "In 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 2\π, 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.",
conference-location = "Foz do Igua{\c{c}}u, PR, Brazil",
conference-year = "29 Oct.-1 Nov. 2018",
language = "en",
ibi = "8JMKD3MGPAW/3S4NEGH",
url = "http://urlib.net/ibi/8JMKD3MGPAW/3S4NEGH",
targetfile = "tss-tsb-descriptors-sibgrapi-wtd-2018 (1).pdf",
urlaccessdate = "2024, May 01"
}