Analyzing Ameliorated Nonnegative Matriz Factorization for Wood Image Representation
Non-negative matrix factorization (NMF) is an unsupervised method whose aim is to find an approximate factorization Vn*m=Wn*r*Hr*m into nonnegative matrices Wn*r and Hr*m. This paper presents an extension to NMF and discusses the development and the use of damped Newton based the non-negative matrix factorization called DNNMF with good convergence properties for wood image representation by adding a diagonal correction to the stiffness matrix and employing a Newton direction in the line search until any constraints become active. This method can make sure the convergence of the cost functions and has been tested with color images based on the LBP feature histograms extracted by Local Binary Pattern (LBP) from the feature subspaces structured by DNNMF. Comparative experiments show that the proposed method is effectual and practical with good research values and potential applications.
Wood Image Representation Nonnegative Matriz Factorization Local Binary Pattern Feature subspaces
Dai-Xian Wu Si-Yuan Wu Zhao Zhang
The Faculty of Computer and Information Science, Southwest University, Chongqing, 400715, China College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Ch School of Information Science and Technology, Nanjing Forestry University, Nanjing, 210037, China
国际会议
北京
英文
2652-2656
2009-08-08（万方平台首次上网日期，不代表论文的发表时间）