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Article

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

1
Graduate School of Information Science and Technology, Hokkaido University, N-14, W-9, Kita-ku, Sapporo, Hokkaido 060-0814, Japan
2
Office of Institutional Research, Hokkaido University, N-8, W-5, Kita-ku, Sapporo, Hokkaido 060-0808, Japan
3
Faculty of Information Science and Technology, Hokkaido University, N-14, W-9, Kita-ku, Sapporo, Hokkaido 060-0814, Japan
*
Authors to whom correspondence should be addressed.
This paper is an extended version of our paper published in: Moroto, Y.; Maeda, K.; Ogawa, T.; Haseyama, M. User-Specific Visual Attention Estimation Based on Visual Similarity and Spatial Information in Images. In the Proceedings of the IEEE International Conference on Consumer Electronics—Taiwan (IEEE 2019 ICCE-TW), Ilan, Taiwan, 20–22 May 2019.
Sensors 2020, 20(8), 2170; https://doi.org/10.3390/s20082170
Received: 15 March 2020 / Revised: 5 April 2020 / Accepted: 9 April 2020 / Published: 11 April 2020
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. View Full-Text
Keywords: personalized saliency map; adaptive image selection; multi-task CNN; object detection personalized saliency map; adaptive image selection; multi-task CNN; object detection
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MDPI and ACS Style

Moroto, Y.; Maeda, K.; Ogawa, T.; Haseyama, M. Few-Shot Personalized Saliency Prediction Based on Adaptive Image Selection Considering Object and Visual Attention. Sensors 2020, 20, 2170. https://doi.org/10.3390/s20082170

AMA Style

Moroto Y, Maeda K, Ogawa T, Haseyama M. Few-Shot Personalized Saliency Prediction Based on Adaptive Image Selection Considering Object and Visual Attention. Sensors. 2020; 20(8):2170. https://doi.org/10.3390/s20082170

Chicago/Turabian Style

Moroto, Yuya, Keisuke Maeda, Takahiro Ogawa, and Miki Haseyama. 2020. "Few-Shot Personalized Saliency Prediction Based on Adaptive Image Selection Considering Object and Visual Attention" Sensors 20, no. 8: 2170. https://doi.org/10.3390/s20082170

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