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dc.contributor.authorKar, A.
dc.contributor.authorMavin, P.
dc.contributor.authorGhaturle, Y.
dc.contributor.authorVani, M.
dc.identifier.citationProceedings - 2017 International Conference on Data Science and Advanced Analytics, DSAA 2017, 2017, Vol.2018-January, , pp.373-381en_US
dc.description.abstractHumans are exposed to many pictures and videos on a daily basis, but they have this exceptional ability to remember the details, even though many of them look very similar. This Video Memorability (VM) is mainly due to distinguishable and a fine representation of the frames in human mind that people tend to remember. Videos have an abundance data contained in the frames which can be used for feature extraction purposes. Each feature from each frame has to be carefully considered to determine the intrinsic property of the video i.e. memorability. Using Convolutional Neural Network (CNN), we propose a solution to the problem of predicting VM, by estimating its memorability. A model has been developed to predict VM using algorithmically extracted features. Two types of features (i) semantic features (ii) visual features have been considered. The effectiveness of the model has been tested using publicly available image and video data. The results confirm that the CNN model can predict memorability with a acceptable performance. � 2017 IEEE.en_US
dc.titleWhat makes a video memorable?en_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

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