Two-dimensional bilinear preserving projections for image feature extraction and classification

• Published in: Neural computing & applications Vol. 24; no. 3-4; pp. 901 - 909
• Main Authors: Li, Yajing, Tan, Zhiming, Zhan, Yongqiang
• Format: Journal Article
• Language: English
• Published: London Springer London 01.03.2014; Springer
• Subjects: Applied sciences; Artificial Intelligence; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Computer science; control theory; systems; Data Mining and Knowledge Discovery; Data processing. List processing. Character string processing; Exact sciences and technology; Image Processing and Computer Vision; Memory organisation. Data processing; Original Article; Pattern recognition. Digital image processing. Computational geometry; Probability and Statistics in Computer Science; Software; Locality preserving criterion; Feature extraction; Manifold learning; Dimensionality; Image processing; Locality; Iterative method; Modeling; Projection method; Dimension reduction; Image analysis; Multidimensional analysis; Vector space; Selection criterion; Reduced order model; Pattern extraction; Image classification
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-012-1311-9

Two-dimensional locality preserving projections (2DLPP) was recently proposed to extract features directly from image matrices based on locality preserving criterion. A significant drawback of 2DLPP is that it only works on one direction (left or right) to reduce the dimensionality of the image matrices and thus too many coefficients are needed for image representation in low-dimensional subspace. In this paper, we propose a novel method called two-dimensional bilinear preserving projections (2DBPP) for image feature extraction. We generalized the image-based (2D-based) feature extraction techniques into bilinear cases, in which 2DLPP is a special case of our proposed method. In order to obtain the bilinear projections, we proposed an iteration method by solving the corresponding generalized eigen-equations. Moreover, analyses show that 2DBPP has stronger locality preserving abilities than 2DLPP. By using the label information and defining different local neighborhood graphs, the proposed framework is further extended to supervised case. Experiments on three databases show that 2DBPP and its supervised extension are superior to some other image-based state-of-the-art techniques.