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Detecting the presence of large buildings in natural images
Malobabić, Jovanka; Le Borgne, Hervé; Murphy, Noel; O'Connor, Noel E.
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.
Keyword(s): Information retrieval; Image processing
Publication Date:
2005
Type: Other
Peer-Reviewed: Unknown
Language(s): English
Institution: Dublin City University
Citation(s): Malobabić, Jovanka, Le Borgne, Hervé ORCID: 0000-0003-0520-8436 <https://orcid.org/0000-0003-0520-8436>, Murphy, Noel and O'Connor, Noel E. ORCID: 0000-0002-4033-9135 <https://orcid.org/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.
File Format(s): application/pdf
Related Link(s): http://doras.dcu.ie/444/1/cbmi_2005.pdf,
http://cbmi05.cs.tut.fi/
First Indexed: 2009-11-05 02:00:42 Last Updated: 2019-02-09 07:03:24