一种基于加权稀疏编码的频域视觉显著性检测算法

 针对现有的基于频域的视觉显著性检测算法检测准确度不高的弱点,本文提出了一种基于加权稀疏编码的频域算法,旨在提高检测准确度的同时保持频域算法运算速度快的优势.在传统的稀疏编码算法基础上,本文根据各子码的增量编码长度来设置它们的权重,实现对图像的加权稀疏编码而不是直接对原始图像进行处理.最后,为了处理多维的稀疏编码信号,本文利用信息论的思想对最新发表的图像签名算法进行了多通道改进,以香农自信息的形式输出图像的显著性检测结果.在公开的人眼跟踪数据库上同9种流行算法的实验对比和对算法复杂度的分析证明了本文算法的有效性和快速性.

A Spectral Algorithm Based on Weighted Sparse Coding for Visual Saliency Detection

A spectral algorithm using weighted sparse coding is proposed for visual saliency detection in this paper.This algorithm is able to improve the accuracy of the traditional spectral saliency detection approaches while preserving their advantage of fast processing speed.Based on the traditional sparse coding algorithm,the sub-codes are weighted according to their incremental coding length.Then,the image is encoded by weighted sparse coding instead of directly transforming the raw images into frequency domain.Finally,we improve the multi-channel method developed in a recently published algorithm called image signature through information theory.Our method yields the saliency map with the form of the Shannon self-information.The experimental comparisons between the proposed and 9 state-of-the-art approaches and the analysis of complexity of our algorithm demonstrate the effectiveness and efficiency of our method.