Filters








30 Hits in 3.7 sec

RUC at MediaEval 2017: Predicting Media Interestingness Task

Shuai Wang, Shizhe Chen, Jinming Zhao, Wenxuan Wang, Qin Jin
2017 MediaEval Benchmarking Initiative for Multimedia Evaluation  
In this paper, we present our methods in the 2017 Predicting Media Interestingness Task.  ...  Predicting the interestingness of images or videos can greatly improve people's satisfaction in many applications, such as video retrieval and recommendations.  ...  INTRODUCTION The interestingness prediction task [1] aims to predict people's general preferences for images and videos, which has a wide range of applications such as video recommendation.  ... 
dblp:conf/mediaeval/WangCZWJ17 fatcat:whgvm77q7fherdbpb3gwborp2e

GIBIS at MediaEval 2017: Predicting Media Interestingness Task

Jurandy Almeida, Ricardo Manhães Savii
2017 MediaEval Benchmarking Initiative for Multimedia Evaluation  
This paper describes the GIBIS team experience in the Predicting Media Interestingness Task at MediaEval 2017.  ...  In this task, the teams were required to develop an approach to predict whether images or videos are interesting or not.  ...  This work is developed in the context of the MediaEval 2017 Predicting Media Interestingness Task, whose goal is to automatically select the most interesting frames or portions of videos according to a  ... 
dblp:conf/mediaeval/AlmeidaS17 fatcat:uvy3mg47d5a2bjmpyoubmx6ujy

TCNJ-CS@MediaEval 2017 Predicting Media Interestingness Task

Sejong Yoon
2017 MediaEval Benchmarking Initiative for Multimedia Evaluation  
In this paper, we present our approach and investigation on the MedialEval 2017 Predicting Media Interestingness Task. We used most of the visual and audiotory features provided.  ...  Official results, as well as our investigation on the task data is provided at the end.  ...  ACKNOWLEDGMENTS This work was supported in part by The College of New Jersey under Support Of Scholarly Activity (SOSA) 2017-2019 grant.  ... 
dblp:conf/mediaeval/Yoon17 fatcat:bngl72a76nd5pboatoibu372ii

LAPI at MediaEval 2017 - Predicting Media Interestingness

Mihai Gabriel Constantin, Bogdan Andrei Boteanu, Bogdan Ionescu
2017 MediaEval Benchmarking Initiative for Multimedia Evaluation  
In the following paper we will present our contribution, approach and results for the MediaEval 2017 Predicting Media Interestingness task.  ...  ACKNOWLEDGMENTS Part of this work was funded by UEFISCDI under research grant PNIII-P2-2.1-PED-2016-1065, agreement 30PED/2017, project SPOT-TER  ...  The MediaEval 2017 Predicting Media Interestingness task [6] creates a benchmarking competition where participants are tasked with the creation of a system that can predict the interestingness of images  ... 
dblp:conf/mediaeval/ConstantinBI17 fatcat:hpxpjyjmcjaz3fiafujrnztsyq

Predicting Media Interestingness via Biased Discriminant Embedding and Supervised Manifold Regression

Yang Liu, Zhonglei Gu, Tobey H. Ko
2017 MediaEval Benchmarking Initiative for Multimedia Evaluation  
In this paper, we describe our model designed for automatic prediction of media interestingness. Specifically, a two-stage learning framework is proposed.  ...  In the second stage, SVM is utilized for prediction. Experimental results validate the effectiveness of our approaches.  ...  In this paper, we propose to use dimensionality reduction to extract low-dimensional features for MediaEval 2017 Predicting Media Interestingness Task.  ... 
dblp:conf/mediaeval/LiuGK17 fatcat:6mwyadp5wrglloni5nyj7g7sne

EURECOM@MediaEval 2017: Media Genre Inference for Predicting Media Interestingness

Olfa Ben Ahmed, Jonas Wacker, Alessandro Gaballo, Benoit Huet
2017 MediaEval Benchmarking Initiative for Multimedia Evaluation  
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.  ...  The media interestingness challenge is organized at MediaEval 2017. The task consists of two subtasks for the prediction of image and video interestingness respectively.  ... 
dblp:conf/mediaeval/AhmedWGH17 fatcat:czfokeqbwzcnrk4iyfy4wa5a5m

Review of Methods to Predict Social Image Interestingness and Memorability [chapter]

Xesca Amengual, Anna Bosch, Josep Lluís de la Rosa
2015 Lecture Notes in Computer Science  
In this paper, the Predicting Media Interestingness task which is running for the second year as part of the MediaEval 2017 Benchmarking Initiative for Multimedia Evaluation, is presented.  ...  All task characteristics are described, namely the task use case and challenges, the released data set and ground truth, the required participant runs and the evaluation metrics.  ...  Part of the task was funded under research grant PN-III-P2-2.1-PED-2016-1065, agreement 30PED/2017, project SPOTTER.  ... 
doi:10.1007/978-3-319-23192-1_6 fatcat:lm6rv7jebvgbtok7umkbmrkbsa

DA-IICT at MediaEval 2017: Objective Prediction of Media Interestingness

Rashi Gupta, Manish Narwaria
2017 MediaEval Benchmarking Initiative for Multimedia Evaluation  
While humans can almost effortlessly rank and judge interestingness of a scene, automated prediction of interestingness for an arbitrary scene is a challenging problem.  ...  Interestingness is defined as the power of engaging and holding the curiosity.  ...  DA-IICT at MediaEval 2017: Objective prediction of media interestingness  ... 
dblp:conf/mediaeval/GuptaN17 fatcat:mqzi36dxwbcrrlcceo3zqv5bx4

The IITB Predicting Media Interestingness System for MediaEval 2017

Jayneel Parekh, Harshvardhan Tibrewal, Sanjeel Parekh
2017 MediaEval Benchmarking Initiative for Multimedia Evaluation  
This paper describes the system developed by team IITB for MediaEval 2017 Predicting Media Interestingness Task.  ...  INTRODUCTION The MediaEval 2017 Predicting Media Interestingness Task [2] deals with automatic selection of images and/or video segments according to their interestingness to a common viewer.  ...  MediaEval'17, 13-15 September 2017, Dublin, Ireland. Training We adopted the following two methods for training: 1.  ... 
dblp:conf/mediaeval/ParekhTP17 fatcat:lbvb2yeup5gclje5ml3xfnastq

Deep learning for multimodal-based video interestingness prediction

Yuesong Shen, Claire-Heiene Demarty, Ngoc Q. K. Duong
2017 2017 IEEE International Conference on Multimedia and Expo (ICME)  
Predicting interestingness of media content remains an important, but challenging research subject.  ...  ., videos from the MediaEval 2016 interestingness task) datasets.  ...  In line with this definition, a first benchmark on Predicting Media Interestingness was recently proposed in the MediaEval 2016 campaign 2 .  ... 
doi:10.1109/icme.2017.8019300 dblp:conf/icmcs/ShenDD17 fatcat:bgqtdwyirbcrzn6hvy2ed7yqdi

Exploring Deep Fusion Ensembling for Automatic Visual Interestingness Prediction [chapter]

Mihai Gabriel Constantin, Liviu-Daniel Stefan, Bogdan Ionescu
2021 Zenodo  
Experimental validation is carried out on a publicly available data set and on the systems benchmarked during the 2017 MediaEval Predicting Media Interestingness task.  ...  While many computer vision and machine learning methods have been tested for predicting media interestingness, overall, due to the heavily subjective nature of interestingness, the precision of the results  ...  Acknowledgement This work was funded under project AI4Media "A European Excellence Centre for Media, Society and Democracy", grant #951911, H2020 ICT-48-2020.  ... 
doi:10.5281/zenodo.5006827 fatcat:5dsrhoaulzhpjf2jix3v3zw5b4

Multi-view Manifold Learning for Media Interestingness Prediction

Yang Liu, Zhonglei Gu, Yiu-ming Cheung, Kien A. Hua
2017 Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval - ICMR '17  
Meanwhile, we introduce a new dimensionality reduction method dubbed Supervised Manifold Regression (SMR) to learn the compact representations for predicting the continuous interestingness levels.  ...  Specifically, we utilize an existing dimensionality reduction method called Neighborhood MinMax Projections (NMMP) to extract the low-dimensional features for predicting the discrete interestingness labels  ...  The MediaEval 2016 Predicting Media Interestingness Task requires participants to automatically select images and/or video segments which are considered to be the most interesting for a common viewer.  ... 
doi:10.1145/3078971.3079021 dblp:conf/mir/LiuGCH17 fatcat:vqe34chcr5gwzkqezwwsrt7u2m

Predicting Interestingness of Visual Content [chapter]

Claire-Hélène Demarty, Mats Sjöberg, Mihai Gabriel Constantin, Ngoc Q. K. Duong, Bogdan Ionescu, Thanh-Toan Do, Hanli Wang
2017 Visual Content Indexing and Retrieval with Psycho-Visual Models  
In this chapter we introduce a benchmarking framework (dataset and evaluation tools) designed specifically for assessing the performance of media interestingness prediction techniques.  ...  These data are annotated by human assessors according to their degree of interestingness.  ...  Part of this work was funded under project SPOTTER PN-III-P2-2.1-PED-2016-1065, contract 30PED/2017.  ... 
doi:10.1007/978-3-319-57687-9_10 fatcat:v462frbpsncsnjvpkajozezrhe

Multimodal fusion for multimedia analysis: a survey

Pradeep K. Atrey, M. Anwar Hossain, Abdulmotaleb El Saddik, Mohan S. Kankanhalli
2010 Multimedia Systems  
This paper describes our approach for the submission to the Mediaeval 2017 Predicting Media Interestingness Task, which was particularly developed for the Image subtask.  ...  As the task ground truth was based on pairwise evaluation of shots or keyframe images within the same movie, next to using precomputed features as-is, we also include a more contextual feature, considering  ...  Due to the similarity of this year's task to the 2016 Predicting Media Interestingness task, we considered the strategies used in submissions to last year's task to inform the strategy of our submission  ... 
doi:10.1007/s00530-010-0182-0 fatcat:qairbgeknvc7tjnnj6cqblozfm

The Predicting Media Memorability Task at MediaEval 2019

Mihai Gabriel Constantin, Bogdan Ionescu, Claire-Hélène Demarty, Ngoc Q. K. Duong, Xavier Alameda-Pineda, Mats Sjöberg
2019 MediaEval Benchmarking Initiative for Multimedia Evaluation  
In this paper, we present the Predicting Media Memorability task, which is running for the second year at the MediaEval 2019 Benchmarking Initiative for Multimedia Evaluation.  ...  Participants are required to create systems that are able to automatically predict the memorability scores of a collection of videos, which should represent the "short-term" and "long-term" memorability  ...  This work was partially supported by the Romanian Ministry of Innovation and Research (UEFISCDI, project SPIA-VA, agreement 2SOL/2017, grant PN-III-P2-2.1-SOL-2016-02-0002).  ... 
dblp:conf/mediaeval/ConstantinIDDAS19 fatcat:oy3bktl5z5asth4uigl37wwpmm
« Previous Showing results 1 — 15 out of 30 results