针对基于对象的图像检索问题，提出一种新的谱聚类多示例学习算法．该算法将图像当作包，将分割区域的视觉特征当作包中的示例，针对正包示例集合进行谱聚类，按聚类中心点数最大原则选择潜在正示例中心和潜在正示例代表，并采用径向基函数和金字塔核分别度量潜在正示例间和其它示例间的相似性，最后利用支持向量机和相关反馈实现图像检索．采用SIVAL 图像集进行的对比实验表明，该方法是有效的．

Proposed in this paper is a novel algorithm based on the spectral clustering and the multiple instance learning,which is applied to the object-based image retrieval. In this algorithm,first,the whole image is regarded as a bag and the visual features of the segmented region are regarded as instances. Next,the spectral clustering of the instance set of positive bags is performed according to the principle of maximum the clustering center number to select the potential center and the representation of positive instance. Then,the radial basis function ( RBF) and the pyramid match kernel ( PMK) are respectively used to measure the similarities of the potential positive instances and other instances in bags. Finally,the support vector machine and the relevance feedback are adopted to retrieve images. Experimental results of the SIVAL image set show that the proposed algorithm is an effective solution to the object-based image retrieval.