Abstract：According to the different importance of features during the feature integration process in modeling computation of visual selective attention, a method of weight estimation for different features is presented to highlight the acceptable saliency region of image in the bottom-up computational model. Firstly, the color, orientation and intensity features are extracted by mimicking the function of feature sensitive neurons of human primary visual cortex. Then the importance of each feature is estimated according to the generalized Gaussian distribution and the variance of its feature map. Finally, the saliency regions are extracted by weighted integration and normalization. The experimental results demonstrate that the proposed method outperforms traditional methods to meet the requirement of observers.
刘琼，秦世引. 视觉选择性注意模型化计算中的特征整合权值估计与图像显著性区域提取[J]. 模式识别与人工智能, 2011, 24(4): 548-554.
Liu Qiong, Qin Shi-Yin. Weight estimation for feature integration and saliency region extraction in modeling computation of visual selective attention. , 2011, 24(4): 548-554.
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