{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T20:46:40Z","timestamp":1761598000483,"version":"3.37.3"},"reference-count":17,"publisher":"World Scientific Pub Co Pte Ltd","issue":"07","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2019,6,30]]},"abstract":"<jats:p> The video highlight detection task is to localize key elements (moments of user\u2019s 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 end-to-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. <\/jats:p>","DOI":"10.1142\/s0218001419400019","type":"journal-article","created":{"date-parts":[[2018,10,26]],"date-time":"2018-10-26T03:22:00Z","timestamp":1540524120000},"page":"1940001","source":"Crossref","is-referenced-by-count":7,"title":["Video Highlight Detection via Region-Based Deep Ranking Model"],"prefix":"10.1142","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8923-6997","authenticated-orcid":false,"given":"Yifan","family":"Jiao","sequence":"first","affiliation":[{"name":"Jiangsu University of Science and Technology, Zhenjiang 212003, P. R. China"},{"name":"National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, P. R. China"}]},{"given":"Tianzhu","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, P. R. China"}]},{"given":"Shucheng","family":"Huang","sequence":"additional","affiliation":[{"name":"Jiangsu University of Science and Technology, Zhenjiang 212003, P. R. China"}]},{"given":"Bin","family":"Liu","sequence":"additional","affiliation":[{"name":"Moshanghua Tech Company, Ltd., Beijing 100030, P. R. China"}]},{"given":"Changsheng","family":"Xu","sequence":"additional","affiliation":[{"name":"National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, P. R. 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