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Detecting the presence of large buildings in natural images

Malobabić, Jovanka, Le Borgne, Hervé orcid logoORCID: 0000-0003-0520-8436, Murphy, Noel and O'Connor, Noel E. orcid logoORCID: 0000-0002-4033-9135 (2005) Detecting the presence of large buildings in natural images. In: CBMI 2005 - 4th International Workshop on Content-Based Multimedia Indexing, 21-23 June 2005, Riga, Latvia.

Abstract
This paper addresses the issue of classification of lowlevel features into high-level semantic concepts for the purpose of semantic annotation of consumer photographs. We adopt a multi-scale approach that relies on edge detection to extract an edge orientation-based feature description of the image, and apply an SVM learning technique to infer the presence of a dominant building object in a general purpose collection of digital photographs. The approach exploits prior knowledge on the image context through an assumption that all input images are �outdoor�, i.e. indoor/outdoor classification (the context determination stage) has been performed. The proposed approach is validated on a diverse dataset of 1720 images and its performance compared with that of the MPEG-7 edge histogram descriptor.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Workshop
Refereed:Yes
Subjects:Computer Science > Information retrieval
Computer Science > Image processing
DCU Faculties and Centres:Research Initiatives and Centres > Centre for Digital Video Processing (CDVP)
Official URL:http://cbmi05.cs.tut.fi/
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Enterprise Ireland, EI 03/IN.3/I361, Ulysse research project ReSEND (FR/2005/56), European Commission FP6-001765
ID Code:444
Deposited On:10 Apr 2008 by DORAS Administrator . Last Modified 09 Nov 2018 10:34
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