Title :
Mahalanobis Distance Metric Based Laplacian Mapping for Image Recognition
Author_Institution :
Coll. of Comput. Sci. & Technol., Harbin Eng. Univ., Harbin, China
Abstract :
An Improved algorithm for image recognition, called Mahalanobis Distance Metric based Laplacian Mapping Algorithm(MLMA), is presented in this paper. Firstly MLMA learns a Mahalanobis metric matrix from training samples, then we use the Mahalanobis metic as a similarity measure in Laplacian Mapping Algorithm. Comparison of MLMA and standard Laplacian Mapping Algorithm in ORL and USPS databases proves that MLMA is more effective and robust than standard Laplacian Mapping Algorithm.
Keywords :
Laplace equations; image recognition; visual databases; MLMA; Mahalanobis distance metric based Laplacian mapping algorithm; Mahalanobis metric matrix; ORL databases; USPS databases; image recognition; Algorithm design and analysis; Databases; Eigenvalues and eigenfunctions; Image recognition; Laplace equations; Measurement; Vectors; dimensionality reduction; image recognition; laplacian mapping; manifold learning;
Conference_Titel :
Internet Computing for Science and Engineering (ICICSE), 2010 Fifth International Conference on
Conference_Location :
Heilongjiang
Print_ISBN :
978-1-4244-9954-0
DOI :
10.1109/ICICSE.2010.25
