A Novel Method for Scanpath Comparison Based on Levenshtein Distance

Modeling the scanpath of human saccade on presentation pictures is a challenging task. To evaluate the consistence of saccadic eye movement sequences, traditional methods, such as scanMatch, transform scanpath data to string sequence with grid-based method and computer the similarity of string sequences using Needleman-Wunsch algorithm. However, scanpath data are different from string data in some aspects, and grid-based method would cause errors in measurement. In the present work, the difference between two symbol sequences was measure by a modified Levenshtein Distance (LD) method. Here, the pair-point distance was computed from position of different fixation points directly, and the distance was further converted to 0 or 1 by a thresholding method. In this way we can adjust the matching threshold value between points and improved the accuracy of LD method by overcoming measurement error of point around grid boundary. Our experimental results demonstrated that our method had better performance than traditional scanMatch.

[1]  Jan Theeuwes,et al.  ScanMatch: A novel method for comparing fixation sequences , 2010, Behavior research methods.

[2]  Jitendra Malik,et al.  An Information Maximization Model of Eye Movements , 2004, NIPS.

[3]  Stephen Lin,et al.  Semantically-Based Human Scanpath Estimation with HMMs , 2013, 2013 IEEE International Conference on Computer Vision.

[4]  S. B. Needleman,et al.  A general method applicable to the search for similarities in the amino acid sequence of two proteins. , 1970, Journal of molecular biology.

[5]  R. Carpenter,et al.  Movements of the Eyes , 1978 .

[6]  T. Foulsham,et al.  Comparing scanpaths during scene encoding and recognition : A multi-dimensional approach , 2012 .

[7]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[8]  Li Xu,et al.  Hierarchical Saliency Detection , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Alan Kingstone,et al.  Recurrence quantification analysis of eye movements , 2013, Behavior Research Methods.

[10]  Jian Sun,et al.  Saliency Optimization from Robust Background Detection , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Asif A. Ghazanfar,et al.  Human-Monkey Gaze Correlations Reveal Convergent and Divergent Patterns of Movie Viewing , 2010, Current Biology.

[12]  Shi-Min Hu,et al.  Global contrast based salient region detection , 2011, CVPR 2011.

[13]  Vladimir I. Levenshtein,et al.  Binary codes capable of correcting deletions, insertions, and reversals , 1965 .

[14]  Alan Kingstone,et al.  A comparison of scanpath comparison methods , 2014, Behavior Research Methods.

[15]  Tamotsu Kasai,et al.  A Method for the Correction of Garbled Words Based on the Levenshtein Metric , 1976, IEEE Transactions on Computers.

[16]  Huchuan Lu,et al.  Saliency Detection via Graph-Based Manifold Ranking , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.