A naive Bayesian method for eye movement recognition of map linear elements

At present, eye tracking technology has been widely used in human-computer interaction, user behavior recognition and prediction, but how to automatically identify user’s eye movement behavior in map reading is still a challenge. This paper proposed a method based on the naive Bayesian classification model to identify the users’ behavior when performing linear feature recognition. We first conducted an eye tracking experiment to acquire users’ eye movement dataset of map reading. Then we extracted and discretized 250 eye movement features involved in the algorithm, and used minimum redundancy maximum relevance algorithm to further select the features. The results show that when the attribute selection method is m=5 using mutual information quotient, the classification accuracy is 78.27%. And when using mutual information difference and m=4, the classification accuracy is 77.01%.We suggested that the proposed method can effectively identify the first elements in the map using eye movement data. This study explores the interaction technology by combining the eye tracking, laying the foundation for the future of designing gaze-controlled interactive map. The proposed method based on naive Bayesian model in this paper is comparable to the existing methods. In addition, the execution efficiency of the model is greatly improved due to the reduction in the number of features. The eye-track recognition algorithm of map reading behavior proposed in this study lays a foundation for future gaze-controlled interactive map research.