Ecole d'ingénieur et centre de recherche en Sciences du numérique

EURECOM @MediaEval 2017: Media genre inference for predicting media interestingness

Ben-Ahmed, Olfa; Wacker, Jonas; Gaballo, Alessandro; Huet, Benoit

MEDIAEVAL 2017, MediaEval Benchmarking Initiative for Multimedia Evaluation, 13-15 September 2017, Dublin, Ireland

In this paper, we present EURECOM's approach to address the MediaEval 2017 Predicting Media Interestingness Task. We developed models for both the image and video subtasks. In particular, we investigate the usage of media genre information (i.e., drama, horror, etc.) to predict interestingness. Our approach is related to the affective impact of media content and is shown to be effective in predicting interestingness for both video shots and key-frames.

Document Bibtex

Titre:EURECOM @MediaEval 2017: Media genre inference for predicting media interestingness
Département:Data Science
Eurecom ref:5345
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Bibtex: @inproceedings{EURECOM+5345, year = {2017}, title = {{EURECOM} @{M}edia{E}val 2017: {M}edia genre inference for predicting media interestingness}, author = {{B}en-{A}hmed, {O}lfa and {W}acker, {J}onas and {G}aballo, {A}lessandro and {H}uet, {B}enoit}, booktitle = {{MEDIAEVAL} 2017, {M}edia{E}val {B}enchmarking {I}nitiative for {M}ultimedia {E}valuation, 13-15 {S}eptember 2017, {D}ublin, {I}reland }, address = {{D}ublin, {IRLANDE}}, month = {09}, url = {} }
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