Abstract
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.
| Original language | English (US) |
|---|---|
| Title of host publication | Computer Vision Systems - 10th International Conference, ICVS 2015, Proceedings |
| Editors | Antonios Gasteratos, Lazaros Nalpantidis, Volker Kruger, Jan-Olof Eklundh |
| Publisher | Springer Verlag |
| Pages | 228-239 |
| Number of pages | 12 |
| ISBN (Print) | 9783319209036 |
| DOIs | |
| State | Published - Jan 1 2015 |
| Externally published | Yes |
| Event | 10th International Conference on Computer Vision Systems, ICVS 2015 - Copenhagen, Denmark Duration: Jul 6 2015 → Jul 9 2015 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 9163 |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Other
| Other | 10th International Conference on Computer Vision Systems, ICVS 2015 |
|---|---|
| Country | Denmark |
| City | Copenhagen |
| Period | 7/6/15 → 7/9/15 |
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Keywords
- Conditional random fields
- Context adding
- Geostatistics
- Underwater vision
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)
Cite this
Geostatistics for context-aware image classification. / Codevilla, Felipe; Botelho, Silvia S.C.; Duarte, Nelson; Purkis, Sam; Shihavuddin, A. S.M.; Garcia, Rafael; Gracias, Nuno.
Computer Vision Systems - 10th International Conference, ICVS 2015, Proceedings. ed. / Antonios Gasteratos; Lazaros Nalpantidis; Volker Kruger; Jan-Olof Eklundh. Springer Verlag, 2015. p. 228-239 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9163).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Geostatistics for context-aware image classification
AU - Codevilla, Felipe
AU - Botelho, Silvia S.C.
AU - Duarte, Nelson
AU - Purkis, Sam
AU - Shihavuddin, A. S.M.
AU - Garcia, Rafael
AU - Gracias, Nuno
PY - 2015/1/1
Y1 - 2015/1/1
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
UR - http://www.scopus.com/inward/record.url?scp=84948956581&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84948956581&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-20904-3_22
DO - 10.1007/978-3-319-20904-3_22
M3 - Conference contribution
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
ER -