Optimal Fisher discriminant analysis using the rank decomposition
Pattern Recognition
Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Discriminant analysis and eigenspace partition tree for face and object recognition from views
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A survey of content-based image retrieval with high-level semantics
Pattern Recognition
Computers in Biology and Medicine
A Comparative Study of PCA, LDA and Kernel LDA for Image Classification
ISUVR '09 Proceedings of the 2009 International Symposium on Ubiquitous Virtual Reality
Face recognition using kernel direct discriminant analysis algorithms
IEEE Transactions on Neural Networks
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In this paper, we propose a novel wavelet based PCA-LDA approach for content Based Image Retrieval. The color and texture features are extracted based on the co-occurrence histograms of wavelet decomposed images. The features extracted by this method form a feature vector of high dimensionality of 1152 for the color image. A combination of Principal Component Analysis (PCA) and Linear Discriminate Analysis (LDA) was applied on feature vector for dimension reduction and to enhance the class separability. By applying PCA to the feature vectors, low dimensionality feature sets were obtained and processed using LDA. The vectors obtained from the LDA are representative of each image. It is evident from the experimental results that the proposed method exhibits superior performance in the reduced feature set (i.e., retrieval efficiency 87% for proposed method, 66% for PCA and 35% for original set based on wavelet feature).