Abstract
This work presents a new object-based visual attention model with bottom-up and top-down features. Bottom-up attention is related to the contrast of primitive visual features, such as color, orientation, and intensity. On the other hand, top-down attention is related to the intentions of the viewer and can be seen as a modulation process through the selection system. Thus, if the viewer is searching for an specific shape or color, the top-down modulation can bias the searching process in relation to those features. Our model is composed of five main modules which are responsible for the extraction of the visual features, image segmentation, object recognition, object-saliency map, and object selection. Results on natural images are compared with state-of-the-art approaches and an ground truth fixation maps for a variety of images revealing the efficacy of the proposed approach for visual attention.
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Benicasa, A.X., Quiles, M.G., Zhao, L., Romero, R.A.F. (2013). Top-Down Biasing and Modulation for Object-Based Visual Attention. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8228. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42051-1_41
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DOI: https://doi.org/10.1007/978-3-642-42051-1_41
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