物体認識と視線解析に基づく視覚的顕著性推定

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2021/01/01

タイトル(英語): Saliency Prediction based on Object Recognition and Gaze Analysis

著者名: 石川　智貴（慶應義塾大学理工学部システムデザイン工学科），矢向　高弘（慶應義塾大学理工学部システムデザイン工学科）

著者名(英語): Tomoki Ishikawa (Department of System Design Engineering, Faculty of Science and Technology, Keio University), Takahiro Yakoh (Department of System Design Engineering, Faculty of Science and Technology, Keio University)

キーワード: 顕著性マップ，視線解析，物体認識，畳み込みニューラルネットワーク　　Saliency Map，Gaze Analysis，Object Recognition，Convolutional Neural Network

要約(英語): Predicting the human visual attention in an image is called saliency prediction, and is an active research area in the field of neuroscience and computer vision. Early works on saliency prediction was performed by using low-level features. In recent years, convolutional neural networks (CNN) have been adapted for saliency prediction and achieved the state-of-the-art performance. However, the eye-gaze depends on the personality of each viewer(1) and conventional methods did not take into account such individual properties of the viewer. Therefore, this paper proposes a novel saliency prediction method considering the influence of eye-gaze. Assuming that personality can be expressed as the degree of attention to an object, our proposed method considers the personality by learning which objects are likely to be perceived by each viewer, and weighting the universal saliency map with the generated mask based on the object detection results. The experimental results show that the proposed universal saliency map achieves higher accuracy than conventional methods on the public dataset, and the proposed weighted saliency map can reflect the variation of the eye-gaze influences among viewers.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.141 No.1 （2021） 特集：電子回路関連技術

本誌掲載ページ: 76-84 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/141/1/141_76/_article/-char/ja/