四川大学学报（自然科学版）Issue(6)：1223-1229,7.DOI:10.3969/j.issn.0490-6756.2013.06.013
基于图像上下文语义信息的场景分类方法
Scene classification based on the contextual semantic information of image
摘要
Abstract
A novel approach was proposed to categorize the scenes .Based on the traditional Bag of Visual words (BOV) model ,the Markov Random Field (MRF) was introduced to combine the feature field and the spatial field in order to quantify the image into a set of unordered visual words .And then the Latent Dirichlet Allocation (LDA ) was used to learn the topic distribution .At last ,the Support Vector Ma-chine(SVM ) was applied to identify a new image .The experimental results on 15 nature scenes show that the introduction of the contextual semantic information on the basis of the traditional method can enhance the classification accuracy .关键词
场景分类/视觉词袋模型/马尔科夫随机场/潜在的狄利克雷分布/支持向量机Key words
scene classification/bag of visual words/markov random field/LDA/SVM分类
信息技术与安全科学引用本文复制引用
温光玉,唐雁,吴梦蝶,黄智兴..基于图像上下文语义信息的场景分类方法[J].四川大学学报（自然科学版）,2013,(6):1223-1229,7.基金项目
教育部“春晖计划” （）
