| Video Highlight Detection via Region-Based Deep Ranking Model | |
Jiao, Yifan1,2; Zhang, Tianzhu2 ; Huang, Shucheng1; Liu, Bin3; Xu, Changsheng2
| |
| Source Publication | INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
 |
| ISSN | 0218-0014 |
| 2019-06-30 | |
| Volume | 33Issue:7Pages:14 |
| Corresponding Author | Huang, Shucheng(schuang2015@gmail.com) |
| Abstract | The video highlight detection task is to localize key elements (moments of user's major or special interest) in a video. Most of the existing highlight detection approaches extract features from the video segment as a whole without considering the difference of local features spatially. In spatial extent, not all regions are worth watching because some of them only contain the background of the environment without human or other moving objects, especially when there is lots of clutter in the background. To deal with this issue, we propose a novel region-based model which can automatically localize the key elements in a video without any extra supervised annotations. Specifically, the proposed model produces position-sensitive score maps for local regions in the spatial dimension of the video segment, and then aggregates all position-wise scores with position-pooling operation. The regions with higher response values will be extracted as key elements. Thus more effective features of the video segment are obtained to predict the highlight score. The proposed position-sensitive scheme can be easily integrated into an endto-end fully convolutional network which aims to update parameters via stochastic gradient descent method in the backward propagation to improve the robustness of the model. Extensive experimental results on the YouTube and SumMe datasets demonstrate that the proposed approach achieves significant improvement over state-of-the-art methods. |
| Keyword | Video highlight detection position-sensitive fully convolutional network |
| DOI | 10.1142/S0218001419400019 |
| WOS Keyword | SCENE DETECTION |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | National Natural Science Foundation of China[61432019] ; National Natural Science Foundation of China[61572498] ; National Natural Science Foundation of China[61532009] ; National Natural Science Foundation of China[61772244] ; Key Research Program of Frontier Sciences, CAS[QYZDJ-SSW-JSC039] ; Beijing Natural Science Foundation[4172062] ; Postgraduate Research & Practice Innovation Program of Jiangsu Province[SJCX17 0599] |
| Funding Organization | National Natural Science Foundation of China ; Key Research Program of Frontier Sciences, CAS ; Beijing Natural Science Foundation ; Postgraduate Research & Practice Innovation Program of Jiangsu Province |
| WOS Research Area | Computer Science |
| WOS Subject | Computer Science, Artificial Intelligence |
| WOS ID | WOS:000470871900001 |
| Publisher | WORLD SCIENTIFIC PUBL CO PTE LTD |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/26013 |
| Collection | 多媒体计算与图形学团队 |
| Corresponding Author | Huang, Shucheng |
| Affiliation | 1.Jiangsu Univ Sci & Technol, Zhenjiang 212003, Jiangsu, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 3.Moshanghua Tech Co Ltd, Beijing 100030, Peoples R China |
| First Author Affilication | Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China |
| Recommended Citation GB/T 7714 | Jiao, Yifan,Zhang, Tianzhu,Huang, Shucheng,et al. Video Highlight Detection via Region-Based Deep Ranking Model[J]. INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE,2019,33(7):14. |
| APA | Jiao, Yifan,Zhang, Tianzhu,Huang, Shucheng,Liu, Bin,&Xu, Changsheng.(2019).Video Highlight Detection via Region-Based Deep Ranking Model.INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE,33(7),14. |
| MLA | Jiao, Yifan,et al."Video Highlight Detection via Region-Based Deep Ranking Model".INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 33.7(2019):14. |
| Files in This Item: | There are no files associated with this item. | |||||
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment