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Completion of Missing Labels for Multi-Label Annotation by a Unified Graph Laplacian Regularization

https://hiroshima.repo.nii.ac.jp/records/2007056
https://hiroshima.repo.nii.ac.jp/records/2007056
4788d226-42a3-4364-8675-987fbf8ddfd7
名前 / ファイル ライセンス アクション
IEICETIS_E103.D_2154.pdf IEICETIS_E103.D_2154.pdf (1.5 MB)
Item type デフォルトアイテムタイプ_(フル)(1)
公開日 2023-03-18
タイトル
タイトル Completion of Missing Labels for Multi-Label Annotation by a Unified Graph Laplacian Regularization
言語 en
作成者 MOJOO, Jonathan

× MOJOO, Jonathan

en MOJOO, Jonathan

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ZHAO, Yu

× ZHAO, Yu

en ZHAO, Yu

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KAVITHA, Muthu Subash

× KAVITHA, Muthu Subash

en KAVITHA, Muthu Subash

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MIYAO, Junichi

× MIYAO, Junichi

en MIYAO, Junichi

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KURITA, Takio

× KURITA, Takio

en KURITA, Takio

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アクセス権
アクセス権 open access
アクセス権URI http://purl.org/coar/access_right/c_abf2
権利情報
権利情報 Copyright © 2020 The Institute of Electronics, Information and Communication Engineers
主題
主題Scheme Other
主題 multi-label image annotation
主題
主題Scheme Other
主題 regularization
主題
主題Scheme Other
主題 missing labels
内容記述
内容記述 The task of image annotation is becoming enormously important for efficient image retrieval from the web and other large databases. However, huge semantic information and complex dependency of labels on an image make the task challenging. Hence determining the semantic similarity between multiple labels on an image is useful to understand any incomplete label assignment for image retrieval. This work proposes a novel method to solve the problem of multi-label image annotation by unifying two different types of Laplacian regularization terms in deep convolutional neural network (CNN) for robust annotation performance. The unified Laplacian regularization model is implemented to address the missing labels efficiently by generating the contextual similarity between labels both internally and externally through their semantic similarities, which is the main contribution of this study. Specifically, we generate similarity matrices between labels internally by using Hayashi's quantification method-type III and externally by using the word2vec method. The generated similarity matrices from the two different methods are then combined as a Laplacian regularization term, which is used as the new objective function of the deep CNN. The Regularization term implemented in this study is able to address the multi-label annotation problem, enabling a more effectively trained neural network. Experimental results on public benchmark datasets reveal that the proposed unified regularization model with deep CNN produces significantly better results than the baseline CNN without regularization and other state-of-the-art methods for predicting missing labels.
言語 en
内容記述
内容記述タイプ Other
内容記述 This work was partly supported by JSPS KAKENHI Grant Number 16K00239.
出版者
出版者 The Institute of Electronics, Information and Communication Engineers
出版者
出版者 電子情報通信学会
言語
言語 eng
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
出版タイプ
出版タイプ VoR
出版タイプResource http://purl.org/coar/version/c_970fb48d4fbd8a85
関連情報
識別子タイプ DOI
関連識別子 10.1587/transinf.2019EDP7318
関連情報
識別子タイプ DOI
関連識別子 https://doi.org/10.1587/transinf.2019EDP7318
収録物識別子
収録物識別子タイプ ISSN
収録物識別子 0916-8532
収録物識別子
収録物識別子タイプ ISSN
収録物識別子 1745-1361
開始ページ
開始ページ 2154
書誌情報 IEICE Transactions on Information and Systems
IEICE Transactions on Information and Systems

巻 E103.D, 号 10, p. 2154-2161, 発行日 2020-10-01
旧ID 51543
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