Markov models for ocular fixation locations in the presence and absence of colour
This work addresses a domain-specific problem in vision science and psychology by modeling eye movements, but it appears incremental as it builds on existing Markov and clustering methods without introducing a new paradigm.
The authors tackled the problem of modeling human eye fixation locations on still images using a Markovian point process, identifying distinct salient regions via k-means clustering and selecting models with Bayes factors, and found that eye behavior differs when color is removed from images.
We propose to model the fixation locations of the human eye when observing a still image by a Markovian point process in R 2 . Our approach is data driven using k-means clustering of the fixation locations to identify distinct salient regions of the image, which in turn correspond to the states of our Markov chain. Bayes factors are computed as model selection criterion to determine the number of clusters. Furthermore, we demonstrate that the behaviour of the human eye differs from this model when colour information is removed from the given image.