Combining Text Semantics and Image Geometry to Improve Scene Interpretation
(2014) 3rd International Conference on Pattern Recognition Applications an Methods (ICPRAM 2014) p.479-486- Abstract
- Inthispaper,wedescribeanovelsystemthatidentifiesrelationsbetweentheobjectsextractedfromanimage. We started from the idea that in addition to the geometric and visual properties of the image objects, we could exploit lexical and semantic information from the text accompanying the image. As experimental set up, we gathered a corpus of images from Wikipedia as well as their associated articles. We extracted two types of objects: human beings and horses and we considered three relations that could hold between them: Ride, Lead, or None. We used geometric features as a baseline to identify the relations between the entities and we describe the improvements brought by the addition of bag-of-wordf eatures and predicate–arguments tructures we... (More)
- Inthispaper,wedescribeanovelsystemthatidentifiesrelationsbetweentheobjectsextractedfromanimage. We started from the idea that in addition to the geometric and visual properties of the image objects, we could exploit lexical and semantic information from the text accompanying the image. As experimental set up, we gathered a corpus of images from Wikipedia as well as their associated articles. We extracted two types of objects: human beings and horses and we considered three relations that could hold between them: Ride, Lead, or None. We used geometric features as a baseline to identify the relations between the entities and we describe the improvements brought by the addition of bag-of-wordf eatures and predicate–arguments tructures we derived from the text. The best semantic model resulted in a relative error reduction of more than 18% over the baseline.
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Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/4352695
- author
- Medved, Dennis LU ; Jiang, Fangyuan LU ; Exner, Peter LU ; Oskarsson, Magnus LU ; Nugues, Pierre LU and Åström, Karl LU
- organization
- publishing date
- 2014
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods
- pages
- 479 - 486
- publisher
- SciTePress
- conference name
- 3rd International Conference on Pattern Recognition Applications an Methods (ICPRAM 2014)
- conference location
- Angers, France
- conference dates
- 2014-03-06 - 2014-03-08
- external identifiers
-
- scopus:84902308102
- ISBN
- 978-989-758-018-5
- language
- English
- LU publication?
- yes
- id
- 3ecf3d61-eac1-4bb9-9de8-6a4411dabe6f (old id 4352695)
- date added to LUP
- 2016-04-04 13:53:48
- date last changed
- 2022-01-30 17:05:49
@misc{3ecf3d61-eac1-4bb9-9de8-6a4411dabe6f, abstract = {{Inthispaper,wedescribeanovelsystemthatidentifiesrelationsbetweentheobjectsextractedfromanimage. We started from the idea that in addition to the geometric and visual properties of the image objects, we could exploit lexical and semantic information from the text accompanying the image. As experimental set up, we gathered a corpus of images from Wikipedia as well as their associated articles. We extracted two types of objects: human beings and horses and we considered three relations that could hold between them: Ride, Lead, or None. We used geometric features as a baseline to identify the relations between the entities and we describe the improvements brought by the addition of bag-of-wordf eatures and predicate–arguments tructures we derived from the text. The best semantic model resulted in a relative error reduction of more than 18% over the baseline.<br/>}}, author = {{Medved, Dennis and Jiang, Fangyuan and Exner, Peter and Oskarsson, Magnus and Nugues, Pierre and Åström, Karl}}, booktitle = {{Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods}}, isbn = {{978-989-758-018-5}}, language = {{eng}}, pages = {{479--486}}, publisher = {{SciTePress}}, title = {{Combining Text Semantics and Image Geometry to Improve Scene Interpretation}}, url = {{https://lup.lub.lu.se/search/files/19728901/4352711.pdf}}, year = {{2014}}, }