This paper describes how to construct a class specific hyper-graph (CSHG) model from a large corpus of multiview images using local invariant features and their spatial configuration. As the first step of this method, each image is represented with a graph, which is constructed from a group of selected robust SIFT features. Secondly, a similarity propagation based graph clustering (SPGC) metgod is then proposed. Using this clustering method,the positive example graphs of a specific class accompanied with a set of negative example graphs are clustered into one or more clusters, which minimize an entropy fimction with a restriction defined on the F-measure. Based on SPGC and the rules of minimizing an entropy function,each cluster is simplified into a tree structure composed of a series of irreducible graphs. Finally, a recognition oriented class specific hyper-graph is generated from the given graph set. Using a trained CSHG model, object recognition can be implemented. Experimental re suits demonstrate the scalability and recognition performance of the neahod.%针对大量不同成像条件下获得的多视图像,研究利用局部不变特征及其空间布局约束构建用于非合作目标识别的类属超图模型的方法.该方法首先将每一幅图像表示为使用选定的稳健SIFT特征构成的属性图,然后提出了一种属性图相似性传播聚类原理.在给定的F度量的约束下,利用该原理进行聚类,并根据熵函数最小化优化条件,可迭代得到特定目标属性图样本集合的最优聚类,进一步将所获得的聚类简化成以非冗余属性图作为节点的类属超图模型.本文用大量图像样本进行了试验测试.实验结果验证了模型的可扩展性和识别性能.
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