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Hierarchical image clustering for analyzing eye tracking videos

The classification of a large number of images is a familiar problem to the image processing community. It occurs in consumer photography, bioinformatics, biomedical imaging, surveillance, and in the field of mobile eye-tracking studies. During eye-tracking studies, what a person looks at is recorded, and for each frame what the person looked at must then be analyzed and classified. In many cases the data analysis time restricts the scope of the studies. This paper describes the initial use of hierarchical clustering of these images to minimize the time required during analysis. Pre-clustering the images allows the user to classify a large number of images simultaneously. The success of this method is dependent on meeting requirements for human-computer-interactions, which are also discussed.