基于局部空间冗余视觉信息抑制的目标识别算法研究

Target recognition algorithm based on local spatial redundancy suppression of visual information

针对图像中相似冗余背景造成的显著目标识别的干扰问题,提出了一种基于超像素的冗余信息抑制的显著目标检测方法。首先,引入超像素的概念,利用超像素优化的空间特征分割图像,获取图像的相似区域;其次,为消除像素间的相关性,计算超像素的香农熵来表示图像的像素信息,并据此建立图像的信息图,最后,为了更有效地去除图像中的相似信息,利用自相似性抑制方法克服冗余信息,建立高效的图像显著图。最后的仿真结果表明,所提算法与传统方法相比,不仅可以准确识别显著目标,而且可以更有效地抑制背景中的冗余信息。

A new method to recognize target of visual attention model based on superpixels and redundancy reduction is proposed, which is about the target recognition problem under the similar redundancy background. Firstly, in order to get similar rigions, the im- age is segmented based on optimized spatial feature of superpixels. Secondly, to eliminate the correlation between pixels, the Shannon entropy is considered to represent pixels information which build up the image information map. Finally, aiming at suppressing the re- peated items effectively, the redundancy of the information map is thrown away to yield the image saliency map using the self-similari- ty. Experimental results show that the proposed model is compared with the state-of-the-art saliency models, this method not only highlights the salient objects in a complex environment but also more effectively suppress the redundancy.