基于全局和局部特征融合的显著性提取方法

显著性提取方法在图像处理、计算机视觉领域有着广泛的应用.然而,基于全局特征和基于局部特征的显著性区域提取算法存在各自的缺点,为此本文提出了一种融合全局和局部特征的显著性提取算法.首先,对图像进行不重叠地分块,当每个图像块经过主成分分析(Principle component analysis,PCA)映射到高维空间后,根据孤立的特征点对应显著性区域的规律得到基于全局特征的显著图;其次,根据邻域内中心块与其他块的颜色不相似性得到基于局部特征的显著图;最后,按照贝叶斯理论将这两个显著图融合为最终的显著图.在公认的三个图像数据库上的仿真实验验证了所提算法在显著性提取和目标分割上比其他先进算法更有效.

Saliency Detection Based on Fusion of Global and Local Features

The saliency detection methods have been widely used in the field of image processing and computer vision. However, the saliency detection algorithms via global feature and local feature extraction have shortcomings. Therefore, a significant saliency detection algorithm is proposed based on fusion of global and local features. Firstly, an image is partitioned to non-overlapped blocks. When each image block is mapped to high dimensional space by principle component analysis(PCA) method, according to the law that the isolated feature points correspond to the salient regions, the saliency map based on the global features is obtained; Secondly, based on the color dissimilarities between center block and its neighborhoods, the saliency map via the local features is obtained; Lastly, based on the Bayes theory, the two obtained saliency maps are fused to the final saliency map. The simulation results on three public image database verify that the proposed algorithm can combine the significant advantages of the global and the local saliency detection algorithms, and it is more effective on saliency detection and object segmentation compared with other state of art algorithms.