Details

Autor(en) / Beteiligte

• BRUCKERT, Alexandre
• LAM, Yat Hong
• CHRISTIE, Marc
• MEUR, Olivier LE

Titel

Deep Learning For Inter-Observer Congruency Prediction

Ist Teil von

• 2019 IEEE International Conference on Image Processing (ICIP), 2019, p.3766-3770

Ort / Verlag

IEEE

Erscheinungsjahr

2019

Quelle

IEEE Electronic Library (IEL)

Beschreibungen/Notizen

• According to the literature regarding visual saliency, observers may exhibit considerable variations in their gaze behaviors. These variations are influenced by aspects such as cultural background, age or prior experiences, but also by features in the observed images. The dispersion between the gaze of different observers looking at the same image is commonly referred as inter-observer congruency (IOC). Predicting this congruence can be of great interest when it comes to study the visual perception of an image. In this paper, we introduce a new method based on deep learning techniques to predict the IOC of an image. This is achieved by first extracting features from an image through a deep convolutional network. We then show that using such features to train a model with a shallow network regression technique significantly improves the precision of the prediction over existing approaches.