HEVC-SVS: Low-level HEVC features and CNN features for TVSum, SumMe, OVP and VSUMM datasets

Proposed HEVC feature sets along with CNN features from GoogleNet, AlexNet, Inception-ResNet-V2, and VGG16 for TVSum, SumMe, OVP and VSUMM datasets. The new modified datasets names are "HEVC-SVS-TVSum", "HEVC-SVS-SumMe", "HEVC-SVS-OVP" and "HEVC-SVS-VSUMM", respectively. The datasets contain the original ground truth data they came with, and these stayed unmodified. Upon using any of these datasets, please do cite our publications where we proposed the HEVC feature set for the first time: If you are using (HEVC-SVS-OVP) and/or (HEVC-SVS-VSUMM) datasets: https://ieeexplore.ieee.org/document/9815254/ @article{issa_cnn_2022, title = {{CNN} and {HEVC} {Video} {Coding} {Features} for {Static} {Video} {Summarization}}, volume = {10}, issn = {2169-3536}, url = {https://ieeexplore.ieee.org/document/9815254/}, doi = {10.1109/ACCESS.2022.3188638}, urldate = {2022-09-29}, journal = {IEEE Access}, author = {Issa, Obada and Shanableh, Tamer}, year = {2022}, pages = {72080--72091}, } If you are using (HEVC-SVS-TVSum) and/or (HEVC-SVS-SumMe) datasets: https://www.mdpi.com/2076-3417/13/10/6065 @article{issa_static_2023, title = {Static {Video} {Summarization} {Using} {Video} {Coding} {Features} with {Frame}-{Level} {Temporal} {Subsampling} and {Deep} {Learning}}, volume = {13}, issn = {2076-3417}, url = {https://www.mdpi.com/2076-3417/13/10/6065}, doi = {10.3390/app13106065}, number = {10}, journal = {Applied Sciences}, author = {Issa, Obada and Shanableh, Tamer}, month = may, year = {2023}, pages = {6065}, } Make sure to also cite the original authors for each of the datasets: TVSum (https://people.csail.mit.edu/yalesong/tvsum/) SumMe (https://gyglim.github.io/me/vsum/index.html) OVP and VSUMM (https://www.sites.google.com/site/vsummsite/download) Acknowledgement: The work in this research project is supported by the American University of Sharjah under research grant number FRG22-E-E44. This research work represents the opinions of the author(s) and does not mean to represent the position or opinions of the American University of Sharjah.本研究提出的高效视频编码（High Efficiency Video Coding, HEVC）特征集，搭配源自GoogleNet、AlexNet、Inception-ResNet-V2及VGG16的卷积神经网络（Convolutional Neural Network, CNN）特征，适用于TVSum、SumMe、OVP与VSUMM数据集。 经修改后的新型数据集依次命名为HEVC-SVS-TVSum、HEVC-SVS-SumMe、HEVC-SVS-OVP与HEVC-SVS-VSUMM。 本系列数据集保留了其原生配套的真值（Ground Truth）数据，且未对其进行任何修改。 若使用本系列数据集，请引用我们首次提出HEVC特征集的相关学术文献： 若使用HEVC-SVS-OVP及/或HEVC-SVS-VSUMM数据集，请引用以下文献： > 文献标识：issa_cnn_2022 > 题名：《卷积神经网络与高效视频编码特征用于静态视频摘要》 > 卷：10 > ISSN：2169-3536 > 链接：https://ieeexplore.ieee.org/document/9815254/ > DOI：10.1109/ACCESS.2022.3188638 > 访问日期：2022年9月29日 > 期刊：IEEE Access > 作者：Obada Issa、Tamer Shanableh > 发表年份：2022 > 页码：72080–72091 若使用HEVC-SVS-TVSum及/或HEVC-SVS-SumMe数据集，请引用以下文献： > 文献标识：issa_static_2023 > 题名：《基于视频编码特征结合帧级时间下采样与深度学习的静态视频摘要》 > 卷：13 > 期：10 > ISSN：2076-3417 > 链接：https://www.mdpi.com/2076-3417/13/10/6065 > DOI：10.3390/app13106065 > 发表月份：5月 > 发表年份：2023 > 期刊：Applied Sciences > 作者：Obada Issa、Tamer Shanableh > 页码：6065 同时，请务必引用各数据集的原始作者及对应链接： - TVSum：https://people.csail.mit.edu/yalesong/tvsum/ - SumMe：https://gyglim.github.io/me/vsum/index.html - OVP与VSUMM：https://www.sites.google.com/site/vsummsite/download 致谢：本研究项目受沙迦美国大学资助，资助编号为FRG22-E-E44。本研究成果仅代表作者个人观点，不代表沙迦美国大学的官方立场与观点。