Visit: an efficient computational model of human visual attention
Visit: an efficient computational model of human visual attention
Toward a computational model of visual attention
Early vision and beyond
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-time visual attention on a massively parallel SIMD architecture
Real-Time Imaging
A Coherent Computational Approach to Model Bottom-Up Visual Attention
IEEE Transactions on Pattern Analysis and Machine Intelligence
Selective visual attention enables learning and recognition of multiple objects in cluttered scenes
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
Assessing the contribution of color in visual attention
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
MAPS: multiscale attention-based presegmentation of color images
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
Optimal Cue Combination for Saliency Computation: A Comparison with Human Vision
IWINAC '07 Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II
Relevance of a feed-forward model of visual attention for goal-oriented and free-viewing tasks
IEEE Transactions on Image Processing
A color saliency model for salient objects detection in natural scenes
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
Selection of a best metric and evaluation of bottom-up visual saliency models
Image and Vision Computing
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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.