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Titlebook: Advances in Multimedia Information Processing – PCM 2017; 18th Pacific-Rim Con Bing Zeng,Qingming Huang,Xiaopeng Fan Conference proceedings

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發(fā)表于 2025-3-21 18:27:46 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱(chēng)Advances in Multimedia Information Processing – PCM 2017
期刊簡(jiǎn)稱(chēng)18th Pacific-Rim Con
影響因子2023Bing Zeng,Qingming Huang,Xiaopeng Fan
視頻videohttp://file.papertrans.cn/150/149020/149020.mp4
學(xué)科分類(lèi)Lecture Notes in Computer Science
圖書(shū)封面Titlebook: Advances in Multimedia Information Processing – PCM 2017; 18th Pacific-Rim Con Bing Zeng,Qingming Huang,Xiaopeng Fan Conference proceedings
影響因子.The two-volume set LNCS 10735 and 10736 constitutes the thoroughly refereed proceedings of the 18th Pacific-Rim Conference on Multimedia, PCM 2017, held in Harbin, China, in September 2017...The 184 full papers presented were carefully reviewed and selected from 264 submissions. The papers are organized in topical sections on: Best Paper Candidate; Video Coding; Image Super-resolution, Debluring, and Dehazing; Person Identity and Emotion; Tracking and Action Recognition; Detection and Classification; Multimedia Signal Reconstruction and Recovery; Text and Line Detection/Recognition; Social Media; 3D and Panoramic Vision; Deep Learning for Signal Processing and Understanding; Large-Scale Multimedia Affective Computing; Sensor-enhanced Multimedia Systems; Content Analysis; Coding, Compression, Transmission, and Processing..
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書(shū)目名稱(chēng)Advances in Multimedia Information Processing – PCM 2017影響因子(影響力)




書(shū)目名稱(chēng)Advances in Multimedia Information Processing – PCM 2017影響因子(影響力)學(xué)科排名




書(shū)目名稱(chēng)Advances in Multimedia Information Processing – PCM 2017網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱(chēng)Advances in Multimedia Information Processing – PCM 2017網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱(chēng)Advances in Multimedia Information Processing – PCM 2017被引頻次




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書(shū)目名稱(chēng)Advances in Multimedia Information Processing – PCM 2017讀者反饋




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Multiple Kernel Learning Based on Weak Learner for Automatic Image Annotationmance, we combine the boosting procedure with the multiple kernel learning to enhance the performance of classifier. We evaluate the proposed method on two benchmark datasets. The experimental results demonstrate that our method is superior to several state-of-the-art methods.
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Conference proceedings 2018dia; 3D and Panoramic Vision; Deep Learning for Signal Processing and Understanding; Large-Scale Multimedia Affective Computing; Sensor-enhanced Multimedia Systems; Content Analysis; Coding, Compression, Transmission, and Processing..
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Marta Peris-Ortiz,Jo?o J. Ferreiraugh our proposed strategy out-performs the Game AI rarely as we did not account game playing-speed that makes a huge difference in victory but at least we succeeded in introducing a strategy that could well compete the Game AI and may defeat it but rarely.
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