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Selective Visual Attention: Computational Models and Applications by Weisi Lin, Liming Zhang

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6.6 Spearman's Rank Order Correlation with Visual Conspicuity

As mentioned in Sections 6.4–6.5, most saliency measurements need to compare the computational saliency map with the ground-truth images or videos obtained from human eye fixations. However, the eye fixations may include both bottom-up information and top-down knowledge that is different for each individual. It seems a bit unfair to test pure bottom-up attention models without prior knowledge. There is no more objective standard for benchmarking the visual conspicuity in a complex scene with or without a target. Experiments showed that search time to a target in a complex natural environment may be related to the conspicuity area measured under conditions of minimal uncertainty [31]. The conspicuity area of a target is defined as the region around the centre of the visual field where the target is capable of attracting visual attention [32]. A target (e.g., red car) among many distractors (e.g., black cars) can be easily detected, even if the eye fixation is located at the target's periphery. Conversely, for the target (e.g., red car) among many red distractors (e.g., red cars), it will fail to attract visual attention. As can be easily understood, the larger the conspicuity area, the faster the search speed. This idea is somewhat different from feature integration theory [11]. The conspicuity area is based on the variations in simple discrimination performance across different stimulus conditions [32], and feature integration ...

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