dc.creator | Khan, Umair Ali | es |
dc.creator | Martínez del Amor, Miguel Ángel | es |
dc.creator | Altowauri, Saleh M. | es |
dc.creator | Ahmed, Adnan | es |
dc.creator | Rahman, Atiq Ur | es |
dc.creator | Sama, Najm Us | es |
dc.creator | Haseeb, Khalid | es |
dc.creator | Islam, Naveed | es |
dc.date.accessioned | 2021-03-22T12:38:42Z | |
dc.date.available | 2021-03-22T12:38:42Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Khan, U.A., Martínez del Amor, M.Á., Altowauri, S.M., Ahmed, A., Rahman, A.U., Sama, N.U.,...,Islam, N. (2020). Movie Tags Prediction and Segmentation Using Deep Learning. IEEE Access, 8, 6071-6086. | |
dc.identifier.issn | 2169-3536 | es |
dc.identifier.uri | https://hdl.handle.net/11441/106404 | |
dc.description.abstract | The sheer volume of movies generated these days requires an automated analytics for ef cient
classi cation, query-based search, and extraction of desired information. These tasks can only be ef ciently
performed by a machine learning based algorithm. We address the same issue in this paper by proposing a
deep learning based technique for predicting the relevant tags for a movie and segmenting the movie with
respect to the predicted tags. We construct a tag vocabulary and create the corresponding dataset in order to
train a deep learning model. Subsequently, we propose an ef cient shot detection algorithm to nd the key
frames in the movie. The extracted key frames are analyzed by the deep learning model to predict the top
three tags for each frame. The tags are then assigned weighted scores and are ltered to generate a compact
set of most relevant tags. This process also generates a corpus which is further used to segment a movie based
on a selected tag. We present a rigorous analysis of the segmentation quality with respect to the number of
tags selected for the segmentation. Our detailed experiments demonstrate that the proposed technique is not
only ef cacious in predicting the most relevant tags for a movie, but also in segmenting the movie with
respect to the selected tags with a high accuracy. | es |
dc.format | application/pdf | es |
dc.format.extent | 15 | es |
dc.language.iso | eng | es |
dc.publisher | IEEE Computer Society | es |
dc.relation.ispartof | IEEE Access, 8, 6071-6086. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Tags prediction | es |
dc.subject | Movie segmentation | es |
dc.subject | Deep learning | es |
dc.subject | Transfer learning | es |
dc.title | Movie Tags Prediction and Segmentation Using Deep Learning | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/submittedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial | es |
dc.relation.publisherversion | https://ieeexplore.ieee.org/document/8947961 | es |
dc.identifier.doi | 10.1109/ACCESS.2019.2963535 | es |
dc.journaltitle | IEEE Access | es |
dc.publication.volumen | 8 | es |
dc.publication.initialPage | 6071 | es |
dc.publication.endPage | 6086 | es |