Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Outdoor and Large-Scale Real-World Scene Analysis - 15th International Workshop on Theoretical Foundations of Computer Vision, Revised Selected Papers |
| Seiten | 264-284 |
| Seitenumfang | 21 |
| Publikationsstatus | Veröffentlicht - 2012 |
| Veranstaltung | 15th International Workshop on Theoretical Foundations of Computer Vision - Dagstuhl Castle, Deutschland Dauer: 26 Juni 2011 → 1 Juli 2011 |
Publikationsreihe
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Band | 7474 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (elektronisch) | 1611-3349 |
Abstract
We present a novel approach to segment and classify objects in images into two classes. A binary conditional random field (CRF) framework is augmented with an unsupervised clustering step learning contextual relations of objects, the so-called implicit scene context (ISC). Several experiments with simulated data, images from benchmark data sets, and aerial images of an urban area show improved results compared to a standard CRF.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Theoretische Informatik
- Informatik (insg.)
- Allgemeine Computerwissenschaft
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Outdoor and Large-Scale Real-World Scene Analysis - 15th International Workshop on Theoretical Foundations of Computer Vision, Revised Selected Papers. 2012. S. 264-284 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 7474 LNCS).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Segmentation and classification of objects with implicit scene context
AU - Wegner, Jan D.
AU - Rosenhahn, Bodo
AU - Sörgel, Uwe
PY - 2012
Y1 - 2012
N2 - We present a novel approach to segment and classify objects in images into two classes. A binary conditional random field (CRF) framework is augmented with an unsupervised clustering step learning contextual relations of objects, the so-called implicit scene context (ISC). Several experiments with simulated data, images from benchmark data sets, and aerial images of an urban area show improved results compared to a standard CRF.
AB - We present a novel approach to segment and classify objects in images into two classes. A binary conditional random field (CRF) framework is augmented with an unsupervised clustering step learning contextual relations of objects, the so-called implicit scene context (ISC). Several experiments with simulated data, images from benchmark data sets, and aerial images of an urban area show improved results compared to a standard CRF.
KW - classification
KW - clustering
KW - conditional random field
KW - context
KW - segmentation
UR - http://www.scopus.com/inward/record.url?scp=84867854149&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-34091-8_12
DO - 10.1007/978-3-642-34091-8_12
M3 - Conference contribution
AN - SCOPUS:84867854149
SN - 9783642340901
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 264
EP - 284
BT - Outdoor and Large-Scale Real-World Scene Analysis - 15th International Workshop on Theoretical Foundations of Computer Vision, Revised Selected Papers
T2 - 15th International Workshop on Theoretical Foundations of Computer Vision
Y2 - 26 June 2011 through 1 July 2011
ER -