计算机工程与科学2018，Vol.40Issue(5)：950-956,7.DOI:10.3969/j.issn.1007-130X.2018.05.026
基于分类融合和关联规则挖掘的图像语义标注
Image annotation based on fusing image classification and frequent patterns mining
秦铭 1蔡明1
作者信息
- 1. 江南大学物联网工程学院,江苏无锡 214122
- 折叠
摘要
Abstract
Automatic image annotation is a highly challenging problem.Base on cross-media relevance model,this paper presents an approach to annotate images by fusing image classification.In the annotation refinement process,the frequent patterns mining algorithm is used to refine the annotation results.Firstly,image features are extracted to generate visual words so as to describe each image.Secondly,the similarity relationship of images is generated by K-means clustering and the classification information is generated by support vector machines.Then,by knowing the relationships between the semantic labels and the images,we can use statistical methods to calculate the probability of each semantic label.The candidate semantic labels are determined by fusing the classification information of the image as weight into the probability.Finally,based on the probability of candidate label words,an improved frequent patterns mining algorithm is used to mine the text relevance degree.The candidate annotation wordset is processed by equal-frequency discretization to obtain the final annotation results.Experiments on Corel image set achieve a better annotation result.关键词
图像标注/K-means聚类/支持向量机/关联规则挖掘Key words
image annotation/K-means clustering/support vector machines/frequent patterns mining分类
信息技术与安全科学引用本文复制引用
秦铭,蔡明..基于分类融合和关联规则挖掘的图像语义标注[J].计算机工程与科学,2018,40(5):950-956,7.
