Detection of compound structures by region group selection from hierarchical segmentations

Date

2016-07

Authors

Akçay, H. Gökhan
Aksoy, Selim

Editor(s)

Advisor

Supervisor

Co-Advisor

Co-Supervisor

Instructor

Citation Stats

Series

Abstract

Detection of compound structures that are comprised of different arrangements of simpler primitive objects has been a challenging problem as commonly used bag-of-words models are limited in capturing spatial information. We have developed a generic method that considers the primitive objects as random variables, builds a contextual model of their arrangements using a Markov random field, and detects new instances of compound structures through automatic selection of subsets of candidate regions from a hierarchical segmentation by maximizing the likelihood of their individual appearances and relative spatial arrangements. In this paper, we extend the model to handle different types of primitive objects that come from multiple hierarchical segmentations. Results are shown for the detection of different types of housing estates in a WorldView-2 image. © 2016 IEEE.

Source Title

International Geoscience and Remote Sensing Symposium, (IGARSS) 2016

Publisher

IEEE

Course

Other identifiers

Book Title

Keywords

Contextual modeling, Markov random field, Object detection, Spatial relationships, Compounds, Buildings, Image edge detection, Markov processes, Image segmentation, Random variables, Context modeling

Degree Discipline

Degree Level

Degree Name

Citation

Published Version (Please cite this version)

Language

English

Type

Conference Paper