生成模型与判别方法相融合的图像分类方法  

Image Categorization of Integrated Generative Models and Discriminative Methods

作　　者：郭立君[1,2,3] 赵杰煜[1,2] 史忠植[1] 

机构地区：[1]中国科学院计算技术研究所,北京100080 [2]宁波大学信息科学与工程学院,浙江宁波315211 [3]中国科学院研究生院,北京100039

出　　处：《电子学报》2010年第5期1141-1145,共5页Acta Electronica Sinica

基　　金：国家973重点基础研究发展规划(No.2007CB311004);国家自然科学基金(No.60672071);浙江省自然科学基金重点项目(No.D1080807);浙江省教育厅科研项目(No.Y200803738)

摘　　要：本文通过在图像局部特征基础上基于高斯混合模型建立全局视觉词汇,用局部特征相对于不同视觉单词的后验概率之和所形成的特征向量来描述图像,最终利用基于线性核的支持向量机进行图像分类.实验中比较了与其它同类方法在PASCAL VOC 2006图像集上的分类结果,验证了本文提出的分类方法及其与目标区域(前景)特征相结合在提高分类效果上的有效性.In our approach, the global visual vocabulary which is similar to keypoints of codebooks is built with Gaussian Mixture Models based on local image features. Images are represented as a new set of feature vectors which are summed posteriori responsibility relative to different visual words. The discriminative classifier is trained by Support Vector Machine with linear kernels based on above features. Experiments were performed on the PASCAL VOC 2006 dataset and the results suggested the influence of background factors on classification effectiveness. And further experiments showed that the features extracted from object areas can be combined effectively to improve classification performance in our method.

关 键 词：生成模型 判别方法 视觉词汇 图像分类 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]