Abstract
Visual attention, as an important issue in computer vision field, has been raised for decades. And many approaches mainly based on the bottom-up or top-down computing models have been put forward to solve this problem. In this paper, we propose a new and effective saliency model which considers the inner opponent relationship of the image information. Inspired by the opponent and feedback mechanism in human perceptive learning, firstly, some opponent models are proposed based on the analysis of original color image information. Secondly, as both positive and negative feedbacks can be learned from the opponent models, we construct the saliency map according to the optimal combination of these feedbacks by using the least square regression with constraints method. Experimental results indicate that our model achieves a better performance both in the simple and complex nature scenes.
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Wang, S., Song, M., Tao, D., Zhang, L., Bu, J., Chen, C. (2011). Opponent and Feedback: Visual Attention Captured. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24965-5_75
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DOI: https://doi.org/10.1007/978-3-642-24965-5_75
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