Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Dynamics of AdaBoost: Cyclic Behavior and Convergence of Margins
The Journal of Machine Learning Research
Non-Iterative Two-Dimensional Linear Discriminant Analysis
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Adaptive Discriminant Projection for Content-based Image Retrieval
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Correlation Metric for Generalized Feature Extraction
IEEE Transactions on Pattern Analysis and Machine Intelligence
2D-LDA: A statistical linear discriminant analysis for image matrix
Pattern Recognition Letters
FADA: an efficient dimension reduction scheme for image classification
PCM'07 Proceedings of the multimedia 8th Pacific Rim conference on Advances in multimedia information processing
Image Classification Using Correlation Tensor Analysis
IEEE Transactions on Image Processing
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Classifier-specific intermediate representation for multimedia tasks
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Robust cross-media transfer for visual event detection
Proceedings of the 20th ACM international conference on Multimedia
Hi-index | 0.00 |
Linear discriminant analysis (LDA) and biased discriminant analysis (BDA) are two effective techniques for dimension reduction, which pay attention to different roles of the positive and negative samples in finding discriminating subspace. However, the drawbacks of these two methods are obvious: LDA has limited efficiency in classifying sample data from subclasses with different distributions, and BDA does not account for the underlying distribution of negative samples. In order to effectively exploit favorable attributes of both BDA and LDA and avoid their unfavorable ones, we propose a novel adaptive discriminant analysis (ADA) for image classification. ADA can find an optimal discriminative subspace with adaptation to different sample distributions. In addition, three novel variants and extensions of ADA are further proposed: 1) Integrated Boosting (i.Boosting), which enhances and combines a set of ADA classifiers into a more powerful one. i.Boosting integrates feature re-weighting, relevance feedback, and AdaBoost into one framework. With affordable computational cost, i.Boosting can provide a unified and stable solution to ADA prediction result. 2) Fast adaptive discriminant analysis (FADA). Instead of searching parameters, FADA can directly find a close-to-optimal projection very fast based on different sample distributions. 3) Two-dimensional adaptive discriminant analysis (2DADA). As opposed to ADA, 2DADA is based on 2-D image matrix representation rather than 1-D vector. So it is simpler, more straightforward, and has lower time complexity to use for image feature extraction. Extensive experiments on synthetic data, UCI benchmark data sets, hand-digit data set, four facial image data sets, and COREL color image data sets show the superior performance of our proposed approaches.