目的 人类视觉系统性能远超当前机器视觉，模拟人类视觉机制改进当前算法是有效研究途径，为此提出一种视觉感知正反馈模型，通过循环迭代、重复叠加视觉刺激生成更符合人类感知的视觉显著性图。方法 首先用多种常规方法检测图像显著度，模拟人类视觉多通道特性，再组合这些显著图为综合显著图；利用显著度大的像素构建初始注视区。其次借助集成RVFL（随机向量功能网络）模拟人脑神经网络产生视觉刺激，对注视与非注视区内像素在线“随机采样—学习建模”，图像像素经模型分类获得新注视区。对新注视区与非注视区，可重复迭代进行“随机采样—学习建模—像素分类”；迭代中若注视区连续相同，则表明感知饱和，迭代终止。若将每次像素分类结果看做是一种视觉刺激，则多次视觉刺激输出叠加，可生成新的图像显著性图。最终的像素分类结果就是图像分割目标。结果 将本文算法与现有方法在标准图像数据库上进行对比评测，包括通过对6种算法在ECSSD、SED2和MSRA10K 3个图像数据库上的P-R曲线，F-measure值和平均绝对误差（MAE）值上进行定量分析，对6种模型生成的显著性图作定性比较。数据表明，本文算法在SED2和MSRA10K图象数据库中性能最好，在ECSSD图象数据库中稍低于BL（bootstrap learning）和RBD（robust background detection）算法。本文算法的显著图与人类视觉感知更接近。且算法的正反馈迭代过程一般可迅速饱和，并未显著增加算法负担。实验结果表明，本文方法可作为一种有效的后处理手段，显著提升常规显著性检测算法的性能。结论 提出了一种模拟人类视觉机制的数据驱动显著性检测算法，无需图像先验知识和事先的标记样本。面对多目标，背景复杂等情况，本文方法具有相对好的鲁棒性和适用性，并且能够较好解决现实环境中图像处理算法的通用性、可靠性和准确性问题。

Objective The performance of current machine vision is inferior to that of human vision. Simulating human visual mechanism can improve existing algorithms. The human visual system can detect objects with high acuity and focus its attention on a region relevant to the current visual task. These advantages are all attributed to the visual attention mechanism. Humans accept attention by making a series of eye movements. Eye movement has two forms: saccades and microsaccades. 1) In the saccades stage, the human eyes aim to find a candidate object, thereby sharply shifting in the entire field of view. 2) While candidates are identified as a target, the eyes will make a series of dense tiny movements called microsaccades around the target to intensify objects and inhibit noises. Continuous microsaccades will lead to visual fading, and the eye movement will switch to the saccades stage to find new objects. The integration of saccades and microsaccades contribute to the rapid and efficient performance of the human vision system. This paper presents a novel saliency detection framework by simulating microsaccades and visual fading. The constructed positive feedback loop focuses on a fixation area and intensifies objects to provide saturation of visual perception that leads to visual fading. In this loop, multiple random sampling of the gaze area is used to simulate the behavior of microsaccades, and random vector functional link networks(RVFL) are utilized to simulate the human neural system to produce binary visual stimulus. The proposed framework is totally data-driven and does not require any prior knowledge and labeled samples. Method First, the conventional saliency detection methods could be used to produce a variety of saliency map. We group these saliency maps to an integrated saliency map to simulate multi-channel visual perception. The integrated saliency map can be subjected to further thresholding to form an initial fixation area. The following multiple random sampling could be executed from the pixels in the fixation and non-fixation area. The ensemble of RVFL is trained on-line by those samples of the pixel. The RVFL model could be used to classify image pixels to obtain a new fixation area(binary area). For the new fixation and non-fixation areas, iterations of "sampling-learning(modeling)-pixel classification" could be performed on-line. If the fixation area is unchanged in the iteration, then this indicates that the perception is saturated and that the iteration should be terminated. When obtaining a binary result of pixel classification as a kind of visual stimulation, the output of multiple visual stimuli could be accumulated to generate new image saliency map. The last binary result of pixel classification in the positive feedback loop could be regard as a foreground of segmentation. Result Three popular image databases, namely SED2, MSRA10K, and ECSSD, were chosen to evaluate the performance of our algorithm. These databases contain a total of 11 100 nature images with different salient objects and scenes. Every image in the dataset was finely labeled manually for saliency detection and image segmentation. Five other models were compared, including the state-of-the-art or closely related models to our approach: BL, RBD, SF, GS, and MR. P-R curve, F-measure, and MAE were used to illustrate the performance of the algorithm in six algorithms on three databases. Experimental results show that our method has the best performance in SED2(two objects) and MSRA10K(single object). Our method is inferior to BL and relatively close to RBD in the ECSSD(complex scene and multi-object) database, while it is better than the rest compared to the other algorithms. The performance of BL, RBD, SF, GS, and MR. can be effectively improved by adding learning-based positive feedback in SED2 database.Experimental images illustrate that the new method is consistent with the visual saliency map of human perception by positive feedback and visual stimulation accumulation. From the view of qualitative evaluation, the binary result detected by our method is clearly closer to the ground truth than others. The positive feedback iteration could be rapidly saturated, and the running time of the algorithm is insignificantly increased. This result can be treated as an effective post-processing modular, which could improve the performance of the conventional saliency detection algorithm. Conclusion This paper proposes a novel saliency region detection method based on machine learning and positive feedback of perception. Motivated by the human visual system, we construct a framework using an RVFL to process visual information from coarse to fine, form a saliency map, and extract salient objects. Our algorithm is totally data-driven and does not require any prior knowledge compared with the existing algorithms. Experiments on several standard image databases show that our method not only improves the performance of the conventional saliency detection algorithms but also successfully segments objects in different scenes.