基于直推式支持向量机的图像检索

Image Retrieval Based on Transductive Support Vector Machine

为缩小图像的低层特征与高层语义之间的语义鸿沟,基于支持向量机的相关反馈机制受到越来越广泛的关注,但这种方法并没有利用未标记样本的隐含信息.为更好地利用这些信息,提出将直推式支持向量机作为反馈过程中的学习算法.通过分析其所用特征向量的特点,设计一种颜色稀疏特征,并将其与纹理特征结合作为图像描述的特征.实验结果表明该方法较令人满意,同时也说明直推式支持向量机可在文本分类以外的领域取得较好结果.

To reduce the gap between low-level image features and high-level semantic concept, support vector machine based relevance feedback draws more and more attentions. However, the information embedded in unlabeled samples is not utilized in that method. In order to exploit these information sufficiently, the transductive support vector machine （TSVM） is introduced into feedback process. Based on analyzing the characters of feature vector for TSVM, a color sparse feature is designed as the image description feature combined with the texture feature. Experimental results show that the proposed method is more discriminative than the feedback process using support vector machine （SVM） , and TSVM obtains good results when applied to other fields.