Open AccessBag‐of‐features for image memorability evaluation
Author(s) -
Lahrache Souad,
El Ouazzani Rajae,
El Qadi Abderrahim
Publication year - 2016
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2015.0383
Subject(s) - computer science , artificial intelligence , pooling , vocabulary , feature (linguistics) , representation (politics) , encode , image (mathematics) , computer vision , pattern recognition (psychology) , bag of words model in computer vision , feature extraction , feature detection (computer vision) , image retrieval , visual word , image processing , politics , philosophy , linguistics , biochemistry , chemistry , political science , law , gene
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.