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Few-Shot Personalized Saliency Prediction Based on Adaptive Image Selection Considering Object and Visual Attention

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Title: Few-Shot Personalized Saliency Prediction Based on Adaptive Image Selection Considering Object and Visual Attention
Authors: Moroto, Yuya Browse this author
Maeda, Keisuke Browse this author
Ogawa, Takahiro Browse this author →KAKEN DB
Haseyama, Miki Browse this author →KAKEN DB
Keywords: personalized saliency map
adaptive image selection
multi-task CNN
object detection
Issue Date: Apr-2020
Publisher: MDPI
Journal Title: Sensors
Volume: 20
Issue: 8
Start Page: 5
Publisher DOI: 10.3390/s20082170
Abstract: A few-shot personalized saliency prediction based on adaptive image selection considering object and visual attention is presented in this paper. Since general methods predicting personalized saliency maps (PSMs) need a large number of training images, the establishment of a theory using a small number of training images is needed. To tackle this problem, although finding persons who have visual attention similar to that of a target person is effective, all persons have to commonly gaze at many images. Thus, it becomes difficult and unrealistic when considering their burden. On the other hand, this paper introduces a novel adaptive image selection (AIS) scheme that focuses on the relationship between human visual attention and objects in images. AIS focuses on both a diversity of objects in images and a variance of PSMs for the objects. Specifically, AIS selects images so that selected images have various kinds of objects to maintain their diversity. Moreover, AIS guarantees the high variance of PSMs for persons since it represents the regions that many persons commonly gaze at or do not gaze at. The proposed method enables selecting similar users from a small number of images by selecting images that have high diversities and variances. This is the technical contribution of this paper. Experimental results show the effectiveness of our personalized saliency prediction including the new image selection scheme.
Rights: http://creativecommons.org/licenses/by/4.0/
Type: article
URI: http://hdl.handle.net/2115/78800
Appears in Collections:情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

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