5.5 Top-down Computation in the Visual Attention System: VOCUS

The visual attention system VOCUS takes its name from ‘Visual Object detection with CompUtational attention System’ proposed by [16, 17, 57]. The book [57] is rooted in the author's PhD thesis, which introduced a goal-directed search model integrating both date-driven (bottom-up) and task-driven (top-down) features. Since the model applies to object detection in real-time robots, its top-down computation and acting on object detection are simpler and more convenient than other top-down models. There are two stages: the training stage and the object search stage. In the training stage, the weights of each extracted feature map are calculated from data-driven features. In the object search stage, excitation and inhibition biasing is used to create a top-down saliency map and a global saliency map by integrating both bottom-up and top-down maps with weights. The weights of bottom-up and top-down features can simulate the extent of human concentration on the demanded task. Although the model was introduced in detail in [57], as a distinctive top-down computation, we still present it in this section.

5.5.1 Bottom-up Features and Bottom-up Saliency Map

The framework of bottom-up feature extraction and bottom-up salient computation of this model is similar to the BS model described in Chapter 3. However, some variations are considered in the bottom-up part.

Firstly, the input colour image is converted into the colour space ...

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