K-Means Clustering Based on Density for Scene Image Classification

摘要：

　　K-means clustering has been extremely popular in scene image classification.However,due to the random selection of initial cluster centers,the algorithm cannot always provide the most optimal results.In this paper,we develop a density-based k-means clustering.First,we calculate the density and distance for each feature vector.Then choose those features with high density and large distance as initial cluster centers.The remaining steps are the same with k-means.In order to evaluate our proposed algorithm,we have conducted several experiments on two-scene image datasets: Fifteen Scene Categories dataset and UIUC Sports Event dataset.The results show that our proposed method has good repeatability.Compared with the traditional k-means clustering,it can achieve higher classification accuracy when applied in multiclass scene image classification.

关键词： K-means Density Scene image classification

作者: Ke Xie Jin Wu Wankou Yang Changyin Sun

作者单位: School of Autmation,Southeast University,Nanjing 210096,China;Key Lab of Measurement and Control of School of Autmation,Southeast University,Nanjing 210096,China;Key Lab of Measurement and Control of

会议类型: 国际会议

会议名称: The 2015 Chinese Intelligent Automation Conference(2015中国智能自动化会议)

会议地点: 福州

会议语种:英文

页码: 379-386

在线出版日期: 2015-05-08（万方平台首次上网日期，不代表论文的发表时间）