Predicting visual fixations on video based on low-level visual features
Predicting visual fixations on video based on low-level visual features
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DOI：
10.1016/j.visres.2007.06.015
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发表时间：
2007-09-01
期刊：
影响因子：
1.8
通讯作者：
Barba, Dominique
中科院分区：
文献类型：
--
作者：
Le Meur, Olivier;Le Callet, Patrick;Barba, Dominique
自下而上视觉注意力的计算模型可以在多大程度上预测观察者正在看什么？低级视觉特征在注意力部署中的贡献是什么？为了回答这些问题，提出了一种新的时空计算模型。该模型融合了多种视觉特征；因此，需要一种融合算法来组合不同的显着图（消色差、彩色和时间）。为了定量评估模型的性能，记录了天真的观察者观看自然动态场景时的眼球运动。使用了四个完成指标。此外，将所提出的模型的预测与最先进的模型 [Itti 模型 (Itti, L., Koch, C., & Niebur, E. (1998). A model of saliency-based Visual Attention for Rapid Scene Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(11), 1254-1259)] 和三个非生物学合理模型的预测进行比较（均匀、闪烁和居中模型）。无论使用何种指标，所提出的模型都比选定的基准模型（中心模型除外）显示出显着的改进。得出了关于低水平视觉特征随时间的影响和眼动追踪实验中的中心偏差的结论。 (C) 2007 Elsevier Ltd. 保留所有权利。
To what extent can a computational model of the bottom-up visual attention predict what an observer is looking at? What is the contribution of the low-level visual features in the attention deployment? To answer these questions, a new spatio-temporal computational model is proposed. This model incorporates several visual features; therefore, a fusion algorithm is required to combine the different saliency maps (achromatic, chromatic and temporal). To quantitatively assess the model performances, eye movements were recorded while naive observers viewed natural dynamic scenes. Four completing metrics have been used. In addition, predictions from the proposed model are compared to the predictions from a state of the art model [Itti's model (Itti, L., Koch, C., & Niebur, E. (1998). A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(11), 1254-1259)] and from three non-biologically plausible models (uniform, flicker and centered models). Regardless of the metric used, the proposed model shows significant improvement over the selected benchmarking models (except the centered model). Conclusions are drawn regarding both the influence of low-level visual features over time and the central bias in an eye tracking experiment. (C) 2007 Elsevier Ltd. All rights reserved.