Interest Estimation for Images Based on Eye Gaze-based Visual and Text Features

This paper presents an estimation method of user-specific interests for images. The proposed method computes a projection which maximizes the correlation between “eye gaze data which are collected while watching images” and “visual and text features” by utilizing Canonical Correlation Analysis (CCA). Since eye gaze data reflect user's interests, new visual and text features calculated by using obtained projections can be also expected to reflect user's interests. Then accurate estimation of user-specific interests for images via Support Vector Machine (SVM) becomes feasible from these features. Experimental results show the effectiveness of our method.

[1]  Jeffrey Pennington,et al.  GloVe: Global Vectors for Word Representation , 2014, EMNLP.

[2]  Qiudan Li,et al.  Personalized search on Flickr based on searcher's preference prediction , 2011, WWW.

[3]  Mohan S. Kankanhalli,et al.  Emotional Attention: A Study of Image Sentiment and Visual Attention , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[4]  Luc Van Gool,et al.  The Interestingness of Images , 2013, 2013 IEEE International Conference on Computer Vision.

[5]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[6]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[7]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Ying Liu,et al.  A survey of content-based image retrieval with high-level semantics , 2007, Pattern Recognit..

[9]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[10]  P. Silvia Interest—The Curious Emotion , 2008 .

[11]  H. Hotelling Relations Between Two Sets of Variates , 1936 .