【摘要】 场景图像分类是计算机视觉领域中的一个基本问题.提出一种基于内容相关性的场景图像分类方法.首先从图像上提取视觉单词,并把图像表示成视觉单词的词频矢量;然后利用产生式模型来学习训练集合中包含的主题,和每一幅图像所包含的相关主题;最后用判定式分类器进行多类学习.提出的方法利用logistic正态分布对主题的相关性进行建模,使得学习得到的类别的主题分布更准确.并且在学习过程中不需要对图像内容进行人工标注.还提出了一种新的局部区域描述方法,它结合了局部区域的梯度信息和彩色信息.在自然场景图像集合和人造场景图像集合上实验了提出的方法,它相对于传统方法取得了更好的结果.更多还原

【Abstract】 Scene image categorization is a basic problem in the field of computer vision.A content correlation based scene image categorization method is proposed in this paper.First of all,dense local features are extracted from images.The local features are quantized to form visual words,and images are represented by the "bag-of-visual words" vector.Then a logistic-normal distribution-based generative model is used to learn themes in the training set,and themes distribution on each image in the training set.Finally,an SVM based discriminative model is used to train the multi-classifier.The proposed approach has the following advantages.Firstly,the approach uses logistic normal distribution as the prior distribution of themes.The correlation of themes is induced by the covariance matrix of logistic normal distribution,which makes the theme distribution of subjects more accurate.Secondly,manually tagging image content is not required in learning process,so as to avoid the heavy human labor and subjective uncertainty introduced in the process of labeling.A new local descriptor is proposed in this paper,which combines the gradient and color information of local area.Experimental results on natural scene dataset and manmade scene dataset show that the proposed scene image categorization method achieves better results than traditional methods.更多还原