2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDSMR-2.6
Paper Title FEW-SHOT PERSONALIZED SALIENCY PREDICTION USING PERSON SIMILARITY BASED ON COLLABORATIVE MULTI-OUTPUT GAUSSIAN PROCESS REGRESSION
Authors Yuya Moroto, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama, Hokkaido University, Japan
SessionSMR-2: Perception and Quality Models
LocationArea F
Session Time:Wednesday, 22 September, 14:30 - 16:00
Presentation Time:Wednesday, 22 September, 14:30 - 16:00
Presentation Poster
Topic Image and Video Sensing, Modeling, and Representation: Perception and quality models for images & video
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract A few-shot personalized saliency prediction method using person similarity based on collaborative multi-output Gaussian process regression is presented in this paper. Contrary to prediction of general saliency maps, that of personalized saliency maps (PSMs), which is a focus of attention owing to its heterogeneity among individuals, is a challenging problem since the amount of training gaze data is limited due to the burden on new persons. Thus, the proposed method focuses on the similarity of gaze tendency between persons. In the proposed method, collaborative Gaussian process regression (CoMOGP) is adopted for PSM prediction. CoMOGP enables to represent similarity of gaze tendency between the target person and other persons as weights, and then consider the similarity for each image by using visual features obtained from images as inputs. The contributions of the few-shot PSM prediction based on CoMOGP are two-folds. 1) CoMOGP, which is one of probabilistic methods, can avoid the overfitting to small amount of training data. 2) Similarity for each image can be considered by using visual features as inputs. In the experiment using the open dataset, the proposed method outperforms comparative methods including the state-of-the-art method.