We study the problem of combining feature maps. Open Access This is an open access article distributed under the CC BY-NC license ( ). The saliency map is sequentially scanned, in order of decreasing saliency, by the focus of attention. This also indicated that prediction method based on FNN proposed by us has a better performance in the level of attention regions' position prediction according to different images. Furthermore, t test results shown that there are significant difference between the results got by two different methods. A user experiment was conducted to evaluate and compare the prediction effect of proposed methods by making surveys for the prediction results. A method for training FNN is also proposed. Therefore, in this paper, the prediction method of the visual attention region inferred by using fuzzy inference and fuzzy neural network (FNN) after extracting and computing of images feature maps and saliency maps were proposed and compared. A lot of models for saliency map combining color, intensity and orientation feature maps by simple normalization and linear summation, which can not reflect the importance of each feature in saliency map well. Saliency determines the capability of an image detail to attract visual attention and thus guide eye movements in a bottom-up way. Visual attention region prediction has been paid much attention by researchers in intelligent systems recent years because it can make the interaction between human and intelligent agents to be more convenient.
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