基于半监督多示例学习的对象图像检索

针对基于对象的图像检索问题,提出一种新的半监督多示例学习(MIL)算法.该算法将图像当作包,分割区域的视觉特征当作包中的示例,按"点密度"最大原则,提取"视觉语义"构造投影空间;然后利用定义的非线性函数将包映射成投影空间中的一个点,以获得图像的"投影特征",并采用粗糙集(RS)方法对其进行属性约简;最后利用直推式支持向量机(TSVM)进行半监督的学习,得到分类器.实验结果表明,该方法有效且性能优于其他方法.

Object-based image retrieval using semi-supervised MIL algorithm

For the problem of object-based image retrieval, a novel semi-supervised multi-instance learning (MIL) algorithm
is presented. This algorithm regards the whole image as a bag, and low-level visual feature of the segmented regions as
instances. Then according to the principle of maximum “local density”, a collect of “visual word”is generated to construct
a projection space. A nonlinear function is defined by using these “visual word” to transform each bag into a point in
the projection space, and extracting all bags’ projection features. Rough set (RS) method is used to reduce the redundant
information in the projection feature, then semi-supervised transductive support vector machine (TSVM) algorithm is applied
to train classifiers. Experimental results on the SIVAL image set show that this algorithm is feasible, and the performance is
superior to other MIL algorithms.