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中文摘要：
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．
英文摘要：
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.
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