CAJ | 학술논문

针对传统“视觉词袋模型”在进行场景分类时只利用图像的特征域，忽略其空间域中上下文语义信息的问题，提出一种基于图像上下文语义信息的场景分类方法。在传统“视觉词袋模型”的基础上，引入马尔科夫随机场模型对图像上下文语义信息进行建模，利用潜在的狄利克雷分布学习场景的主题分布，且利用支持向量机构造场景分类器。对15类场景的分类实验证明该方法能够有效提高分类精确度。
침대전통“시각사대모형”재진행장경분류시지이용도상적특정역，홀략기공간역중상하문어의신식적문제，제출일충기우도상상하문어의신식적장경분류방법。재전통“시각사대모형”적기출상，인입마이과부수궤장모형대도상상하문어의신식진행건모，이용잠재적적리극뢰분포학습장경적주제분포，차이용지지향량궤구조장경분류기。대15류장경적분류실험증명해방법능구유효제고분류정학도。
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 .