Lahrache, S. and El Ouazzani, R. and El Qadi, A. (2016) Bag-of-features for image memorability evaluation. IET Computer Vision, 10 (6). pp. 577-584.

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

Image memorability represents the degree to which images are remembered or forgotten after a period of time. Studying image memorability in computer vision is the task of finding special characteristics in memorable images, in order to develop a representative model of this type of images. Several approaches have been realised to examine features that can affect image memorability. In this study, the authors use bag-of-features as another kind of visual feature descriptor to assess image memorability. The authors' method based on bag-of-visual-words (BoVWs) technique involves four main steps. First, the authors extract local image features from regions/points of interest which are automatically detected. Then, they encode these local features by mapping them to a created visual vocabulary. Later, the authors apply features pooling and normalisation techniques to obtain image BoVW representation. Finally, the authors use this representation to examine image memorability as a problem of classification. They present different implementation choices for each step and compare reached results. The authors' method performs best significant results in comparison with other approaches found in literature. © The Institution of Engineering and Technology.

Item Type: Article
Uncontrolled Keywords: Computer science; Software engineering, Bag of features; Bag-of-visual-words; Local feature; Local image features; Normalisation; Visual feature; Visual vocabularies, Computer vision
Subjects: Computer Science
Divisions: SCIENTIFIC PRODUCTION > Computer Science
Depositing User: Administrateur Eprints Administrateur Eprints
Last Modified: 31 Jan 2020 15:46
URI: http://eprints.umi.ac.ma/id/eprint/2573

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