Abstract:
The multi-class object recognition method usually suffers from the problem of curse dimensionality after extracting feature. This paper proposes a new approach for learning a discriminative model of object classes combined affinity propagation clustering. By using Affinity Propagation(AP) , a representative visual vocabulary can be obtained. Experiment in Sowerby databases shows that it is superior to the recognition rate of k-means algorithms .
Key words:
Affinity Propagation(AP) clustering,
multi-class object recognition,
visual vocabulary
摘要: 多类物体识别在提取特征之后,样本的数量会呈指数倍增加,为减少计算量同时,不降低识别率,采用亲和传递算法对样本数据进行聚类形成视觉字典,帮助并提升物体识别效率。在Sowerby图像数据库上进行实验证明,该方法与使用k均值聚类建立视觉字典方法相比,在同等条件下具有更高的识别率。
关键词:
亲和传递聚类,
多类物体识别,
视觉字典
CLC Number:
DAI Song; LI Wei-sheng. Multi-class Object Recognition Method Based on Affinity Propagation Clustering[J]. Computer Engineering, 2009, 35(14): 206-208.
代 松;李伟生. 基于亲和传递聚类的多类物体识别方法[J]. 计算机工程, 2009, 35(14): 206-208.