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. 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. 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. 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.