Identity statement area
Reference TypeConference Paper (Conference Proceedings)
Last Update2017: administrator
Metadata Last Update2020: administrator
Citation KeyBastosSchw:2017:AsReIm
TitleAssigning Relative Importance to Scene Elements
Access Date2021, Jan. 25
Number of Files1
Size2907 KiB
Context area
Author1 Bastos, Igor Leonardo Oliveira
2 Schwartz, William Robson
Affiliation1 Universidade Federal de Minas Gerais
2 Universidade Federal de Minas Gerais
EditorTorchelsen, Rafael Piccin
Nascimento, Erickson Rangel do
Panozzo, Daniele
Liu, Zicheng
Farias, Mylène
Viera, Thales
Sacht, Leonardo
Ferreira, Nivan
Comba, João Luiz Dihl
Hirata, Nina
Schiavon Porto, Marcelo
Vital, Creto
Pagot, Christian Azambuja
Petronetto, Fabiano
Clua, Esteban
Cardeal, Flávio
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ
DateOct. 17-20, 2017
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Tertiary TypeFull Paper
History2017-08-21 17:53:01 :: -> administrator :: 2017
2020-02-19 02:01:26 :: administrator -> :: 2017
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
Content TypeExternal Contribution
Keywordshuman perception, context, importance of elements in a scene.
AbstractThe human brain is able to rapidly understand scenes through the recognition of their composing elements and comprehension of the role that each of them plays. This process, related to human perception, impacts in what people care when they see an image and the priority they give to each element. The idea of priority, also referred as importance, is based on biological features of perception and social aspects that interfere in how people perceive what they see and what is considered relevant. In this context, this paper proposes the Element Importance Relative Assignment (EIRA), an approach that models how humans attribute importance to elements in a scene. This approach is based on perceptual, compositional and contextual features employed to assign importance to elements in a scene. To evaluate the proposed approach, tests were conducted in different image datasets, with emphasis on the UIUC Pascal Sentence Dataset, where our approach achieved an average accuracy of 86.89%.
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Next Higher Units8JMKD3MGPAW/3PJT9LS
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