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Salient Object Detection With Importance Degree
https://uec.repo.nii.ac.jp/records/9677
https://uec.repo.nii.ac.jp/records/9677cda44e2c-ed8a-4444-98f9-2f42f8e0360b
名前 / ファイル | ライセンス | アクション |
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I専攻吉田_文献No.1の著者最終稿 (3.9 MB)
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Item type | 学術雑誌論文 / Journal Article(1) | |||||
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公開日 | 2020-11-18 | |||||
タイトル | ||||||
言語 | en | |||||
タイトル | Salient Object Detection With Importance Degree | |||||
言語 | ||||||
言語 | eng | |||||
キーワード | ||||||
言語 | en | |||||
主題 | Saliency detection | |||||
キーワード | ||||||
言語 | en | |||||
主題 | salient object detection | |||||
キーワード | ||||||
言語 | en | |||||
主題 | instance segmentation | |||||
キーワード | ||||||
言語 | en | |||||
主題 | convolutional neural network (CNN) | |||||
キーワード | ||||||
言語 | en | |||||
主題 | rank correlation metric | |||||
資源タイプ | ||||||
資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
資源タイプ | journal article | |||||
著者 |
Umeki, Yo
× Umeki, Yo× Funahashi, Isana× Yoshida, Taichi× Iwahashi, Masahiro |
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抄録 | ||||||
内容記述タイプ | Abstract | |||||
内容記述 | In this article, we introduce salient object detection with importance degree (SOD-ID), which is a generalized technique for salient object detection (SOD), and propose an SOD-ID method. We define SOD-ID as a technique that detects salient objects and estimates their importance degree values. Hence, it is more effective for some image applications than SOD, which is shown via examples. The definition, evaluation procedure, and data collection for SOD-ID are introduced and discussed, and we propose its evaluation metric and data preparation, whose validity is discussed with the simulation results. Moreover, we propose an SOD-ID method, which consists of three technical blocks: instance segmentation, saliency detection, and importance degree estimation. The saliency detection block is proposed based on a convolutional neural network using the results of the instance segmentation block. The importance degree estimation block is achieved using the results of the other blocks. The proposed method accurately suppresses inaccurate saliencies and estimates the importance degree for multi-object images. In the simulations, the proposed method outperformed state-of-the-art methods with respect to the F-measure for SOD; and Spearman's and Kendall rank correlation coefficients, and the proposed metric for SOD-ID. | |||||
書誌情報 |
en : IEEE Access 巻 8, p. 147059-147069, 発行日 2020-08-07 |
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出版者 | ||||||
出版者 | IEEE | |||||
ISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 21693536 | |||||
DOI | ||||||
関連タイプ | isVersionOf | |||||
識別子タイプ | DOI | |||||
関連識別子 | 10.1109/ACCESS.2020.3014886 | |||||
権利 | ||||||
権利情報 | (c) 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This is not the published version. Please cite only the published version. | |||||
関連サイト | ||||||
識別子タイプ | DOI | |||||
関連識別子 | https://doi.org/10.1109/ACCESS.2020.3014886 | |||||
著者版フラグ | ||||||
出版タイプ | AM | |||||
出版タイプResource | http://purl.org/coar/version/c_ab4af688f83e57aa |