摘要
在图像检索领域当中,低层视觉特征和高层语义之间的"语义鸿沟"问题都是许多研究学者面前的一大障碍。相关反馈机制在一定程度上解决了"语义鸿沟"问题,同时相关反馈也存在反馈次数过多,反馈效果不够理想等问题。为解决上述问题,提出一种贝叶斯和模糊语义相关矩阵(FSRM)相结合的反馈算法。实现方法是:用贝叶斯分类器对图像库进行分类,达到压缩图像库的目的,然后用模糊语义相关矩阵对压缩之后的图像库进行检索,并反馈最终结果。研究结果表明,与贝叶斯算法和FSRM相比,本文提出的算法明显地提高了反馈效果,优化了反馈次数。
The semantic gap,which exists between low level visual features and high level semantic concepts,is an obstacle to the development of image retrieval. The semantic gap is narrowed by relevant feedback techniques to some extent. However,the image retrieval process with the relevant feedback techniques also has many disadvantages such as too many feedback times or unsatisfactory feedback effect. In order to improve the relevance feedback method,a new relevance feedback strategy combining Bayesian and FSRM technology has been presented. The main approach was achieved firstly by assorting the image library with the Bayesian classifier compressing the image library; secondly,by searching the compressed image library with the FSRM; and lastly,by returning the worked out results. The experiment results illustrated the accuracy of the feedback method and showed it to be the best compared with FSRM algorithm and Bayesian algorithm.
出处
《网络新媒体技术》
2018年第1期22-26,39,共6页
Network New Media Technology
基金
国家自然科学基金项目(61761042)
陕西省高水平大学建设项目(2015SXTS02)
延安大学自然基金项目(YDQ2016-25)
陕西省大学生创新计划项目(1568)
关键词
图像检索
相关反馈
贝叶斯(方法)
模糊语义相关矩阵
正态分布
image retrieval
relevance feedback
Bayesian（method）
Fuzzy semantic relevance matrix
normal distribution
