【摘要】 在传统的基于内容图像检索的方法中 ,由于图像的领域较宽 ,图像的低级视觉特征和高级概念之间存在着较大的语义间隔 ,导致检索效果不佳 .该文认为更有现实意义的做法是 ,缩窄图像的领域以减小低级特征和高级概念间的语义间隔 ,并利用机器学习方法自动建立图像类的模型 ,从而提供用户概念化的图像查询方式 .该文以自然图像领域为例 ,使用支持向量机 (SVM )学习自然图像的类别 ,学习到的模型用于自然图像分类和检索 .实验结果表明作者的方法是可行的 .更多还原

【Abstract】 In the traditional approach of content-based image retrieval, the wide image domain results in the wide semantic gap between the low-level features and the high-level concepts. We propose to narrow the image domain and use machine learning methods to automatically construct models for image classes, thus providing users with a conceptualized way to image query. In this paper, support vector machines are trained for natural image classification. The resulting image class models are incorporated into image retrieval system, so that the users can search natural images by classes. The experimental results are promising.更多还原