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

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5.6 Hybrid Model of Bottom-up Saliency with Top-down Attention Process

Another top-down model with learning function and visual memory is proposed in [18, 59–63]. In its bottom-up saliency map, symmetry is considered as a low-level feature. The four conspicuity maps in the model: intensity, colour, orientation and symmetry are generated, and processed by independent component analysis (ICA) [64, 65] in order to reduce redundancy. The visual memory that stores the top-down knowledge adopts a fuzzy adaptive resonance theory (ART) neural network with learning function [66]. The pattern input to the fuzzy ART neural network comprises the conspicuity maps from the bottom-up processing before ICA filtering. There are two fuzzy ART networks to memorize the knowledge about the objects: the reinforced part and the inhibited part. In the training stage, the conspicuity maps in the most salient area, computed by the pure bottom-up model, are given as inputs of the fuzzy ART network. Depending on the label that the supervisor gives to a salient area, as an interesting or unwanted area, the features are given as inputs to the reinforced or the inhibited part, respectively. Thereby, the fuzzy ART network memorizes the features of both interesting and unwanted areas in the training stage. Thus, in the testing stage, it becomes easy to decide whether a new salient area is interesting to the supervisor or not, based on the top-down information. The model is easily realized in the real world because ...

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