TY - GEN
T1 - Geostatistics for context-aware image classification
AU - Codevilla, Felipe
AU - Botelho, Silvia S.C.
AU - Duarte, Nelson
AU - Purkis, Samuel
AU - Shihavuddin, A. S.M.
AU - Garcia, Rafael
AU - Gracias, Nuno
N1 - Funding Information:
Additional support was granted by the Spanish National Project OMNIUS (CTM2013-46718-R), and the Generalitat de Catalunya through the TECNIOspring program (TECSPR14-1-0050) to N. Gracias.
Funding Information:
The authors would like to thank to the Brazilian National Agency of Petroleum, Natural Gas and Biofuels(ANP), to the Funding Authority for Studies and Projects(FINEP) and to Ministry of Science and Technology (MCT) for their financial support through the Human Resources Program of ANP to the Petroleum and Gas Sector - PRH-ANP/MCT.
Funding Information:
This paper is also a contribution of the Brazilian National Institute of Science and Technology - INCT-Mar COI funded by CNPq Grant Number 610012/2011-8.
Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - Context information is fundamental for image understanding. Many algorithms add context information by including semantic relations among objects such as neighboring tendencies, relative sizes and positions. To achieve context inclusion, popular context-aware classification methods rely on probabilistic graphical models such as Markov Random Fields (MRF) or Conditional Random Fields (CRF). However, recent studies showed that MRF/CRF approaches do not perform better than a simple smoothing on the labeling results. The need for more context awareness has motivated the use of different methods where the semantic relations between objects are further enforced. With this, we found that on particular application scenarios where some specific assumptions can be made, the use of context relationships is greatly more effective. We propose a new method, called GeoSim, to compute the labels of mosaic images with context label agreement. Our method trains a transition probability model to enforce properties such as class size and proportions. The method draws inspiration from Geostatistics, usually used to model spatial uncertainties. We tested the proposed method in two different ocean seabed classification context, obtaining state-of-art results.
AB - Context information is fundamental for image understanding. Many algorithms add context information by including semantic relations among objects such as neighboring tendencies, relative sizes and positions. To achieve context inclusion, popular context-aware classification methods rely on probabilistic graphical models such as Markov Random Fields (MRF) or Conditional Random Fields (CRF). However, recent studies showed that MRF/CRF approaches do not perform better than a simple smoothing on the labeling results. The need for more context awareness has motivated the use of different methods where the semantic relations between objects are further enforced. With this, we found that on particular application scenarios where some specific assumptions can be made, the use of context relationships is greatly more effective. We propose a new method, called GeoSim, to compute the labels of mosaic images with context label agreement. Our method trains a transition probability model to enforce properties such as class size and proportions. The method draws inspiration from Geostatistics, usually used to model spatial uncertainties. We tested the proposed method in two different ocean seabed classification context, obtaining state-of-art results.
KW - Conditional random fields
KW - Context adding
KW - Geostatistics
KW - Underwater vision
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U2 - 10.1007/978-3-319-20904-3_22
DO - 10.1007/978-3-319-20904-3_22
M3 - Conference contribution
AN - SCOPUS:84948956581
SN - 9783319209036
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 228
EP - 239
BT - Computer Vision Systems - 10th International Conference, ICVS 2015, Proceedings
A2 - Gasteratos, Antonios
A2 - Nalpantidis, Lazaros
A2 - Kruger, Volker
A2 - Eklundh, Jan-Olof
PB - Springer Verlag
T2 - 10th International Conference on Computer Vision Systems, ICVS 2015
Y2 - 6 July 2015 through 9 July 2015
ER -