Authors:
Sahil Saroop
1
;
Herna L. Viktor
1
;
Patrick McCurdy
1
and
Eric Paquet
2
Affiliations:
1
University of Ottawa, Canada
;
2
University of Ottawa and National Research Council, Canada
Keyword(s):
Content-based Retrieval, Multimedia Data Mining, Bitumen Sands, Multi-domain Classification, Class Imbalance.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Clustering and Classification Methods
;
Computational Intelligence
;
Evolutionary Computing
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Mining Multimedia Data
;
Soft Computing
;
Symbolic Systems
Abstract:
Debates over Canada’s energy future with its oil sands has become a flashpoint of public interest. Stakeholders have identified advantages, such as economic benefit and global energy demand, and drawbacks, notably environmental and social challenges. This research focuses on discovering how various organizations employ graphics, images and videos in the media, in order to further our understanding of the context and evolution of the oil sands discourse, since the late 1960s. To this end, we created the open-source Mediatoil database contains images from six categories of imagery, namely graphics, machines, people, landscape, protest and open-pit. We further created the Mediatoil-IR content-based image retrieval system that utilizes SURF descriptors and bags of features. We illustrate how the Mediatoil-IR system was used in order to explore and to contrast the imagery used by the various stakeholders, within a multi-class learning setting. Our experimental results show that dividing t
he images into sub-categories is beneficial for retrieval and classification.
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