Theory of T-norms and fuzzy inference methods
Fuzzy Sets and Systems - Special memorial volume on fuzzy logic and uncertainly modelling
Can relevance of images be inferred from eye movements?
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Implicit emotional tagging of multimedia using EEG signals and brain computer interface
WSM '09 Proceedings of the first SIGMM workshop on Social media
GaZIR: gaze-based zooming interface for image retrieval
Proceedings of the 2009 international conference on Multimodal interfaces
Image annotation through gaming (TAG4FUN)
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
Image ranking with implicit feedback from eye movements
Proceedings of the 2010 Symposium on Eye-Tracking Research & Applications
| Hi-index | 0.00 |
An innovative model is proposed that empowers the semi-automatic image annotation algorithms with the implicit feedback of the users' eyes. This frame work extracts the features from the users' gaze pattern over an image by the help of the eye-trackers and combines them with the low level feature properties of that image. The resulting feature vector is sent to a fuzzy inference framework which grades the users' interest in the visited images. By defining a threshold in the middle of the interest level, the images can be classified in case the user is searching for a target concept. In addition to classifying the user's interest level enables us to cluster the visited images according to the users concerns. The primary results show that this model can classify the images with a F1 measure over 0.52.