Linea vs. Nonlinear Feature Combination for Saliency Computation: A Comparison with Human Vision

In the heart of the computer model of visual attention, an interest or saliency map is derived from an input image in a process that encompasses several data combination steps. While several combination strategies are possible and the choice of a method influences the final saliency substantially, there is a real need for a performance comparison for the purpose of model improvement. This paper presents contributing work in which model performances are measured by comparing saliency maps with human eye fixations. Four combination methods are compared in experiments involving the viewing of 40 images by 20 observers. Similarity is evaluated qualitatively by visual tests and quantitatively by use of a similarity score. With similarity scores lying 100% higher, non-linear combinations outperform linear methods. The comparison with human vision thus shows the superiority of non-linear over linear combination schemes and speaks for their preferred use in computer models.


Published in:
Pattern Recognition, 4174
Year:
2006
Publisher:
Elsevier
ISSN:
0031-3203
Laboratories:


Note: The status of this file is: EPFL only


 Record created 2011-07-29, last modified 2018-01-28

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