@article {12612,
	title = {Principal components null space analysis for image and video classification},
	journal = {Image Processing, IEEE Transactions on},
	volume = {15},
	year = {2006},
	month = {2006/07//},
	pages = {1816 - 1830},
	abstract = {We present a new classification algorithm, principal component space analysis (PCNSA), which is designed for classification problems like object recognition where different classes have unequal and nonwhite noise covariance matrices. PCNSA first obtains a principal components subspace (PCA space) for the entire data. In this PCA space, it finds for each class "i", an M<sub>i</sub>-dimensional subspace along which the class{\textquoteright} intraclass variance is the smallest. We call this subspace an approximate space (ANS) since the lowest variance is usually "much smaller" than the highest. A query is classified into class "i" if its distance from the class{\textquoteright} mean in the class{\textquoteright} ANS is a minimum. We derive upper bounds on classification error probability of PCNSA and use these expressions to compare classification performance of PCNSA with that of subspace linear discriminant analysis (SLDA). We propose a practical modification of PCNSA called progressive-PCNSA that also detects "new" (untrained classes). Finally, we provide an experimental comparison of PCNSA and progressive PCNSA with SLDA and PCA and also with other classification algorithms-linear SVMs, kernel PCA, kernel discriminant analysis, and kernel SLDA, for object recognition and face recognition under large pose/expression variation. We also show applications of PCNSA to two classification problems in video-an action retrieval problem and abnormal activity detection.},
	keywords = {approximate null space;classification error probability;face recognition;image classification;object recognition;principal components null space analysis;subspace linear discriminant analysis;video classification;image classification;principal component a, Automated;Principal Component Analysis;Signal Processing, Computer-Assisted;Information Storage and Retrieval;Models, Computer-Assisted;Video Recording;, Statistical;Pattern Recognition},
	isbn = {1057-7149},
	doi = {10.1109/TIP.2006.873449},
	author = {Vaswani, N. and Chellapa, Rama}
}
@article {12656,
	title = {Background learning for robust face recognition with PCA in the presence of clutter},
	journal = {Image Processing, IEEE Transactions on},
	volume = {14},
	year = {2005},
	month = {2005/06//},
	pages = {832 - 843},
	abstract = {We propose a new method within the framework of principal component analysis (PCA) to robustly recognize faces in the presence of clutter. The traditional eigenface recognition (EFR) method, which is based on PCA, works quite well when the input test patterns are faces. However, when confronted with the more general task of recognizing faces appearing against a background, the performance of the EFR method can be quite poor. It may miss faces completely or may wrongly associate many of the background image patterns to faces in the training set. In order to improve performance in the presence of background, we argue in favor of learning the distribution of background patterns and show how this can be done for a given test image. An eigenbackground space is constructed corresponding to the given test image and this space in conjunction with the eigenface space is used to impart robustness. A suitable classifier is derived to distinguish nonface patterns from faces. When tested on images depicting face recognition in real situations against cluttered background, the performance of the proposed method is quite good with fewer false alarms.},
	keywords = {Automated;Principal Component Analysis;Reproducibility of Results;Sensitivity and Specificity;Signal Processing, Biological;Models, Computer-Assisted;Information Storage and Retrieval;Models, Computer-Assisted;Subtraction Technique;, PCA;background learning;clutter;eigenface recognition method;face recognition;linear discriminant;principal component analysis;clutter;eigenvalues and eigenfunctions;face recognition;learning (artificial intelligence);principal component analysis;Algorith, Statistical;Pattern Recognition},
	isbn = {1057-7149},
	doi = {10.1109/TIP.2005.847288},
	author = {Rajagopalan, AN and Chellapa, Rama and Koterba, N.T.}
}
@article {13592,
	title = {Machine printed text and handwriting identification in noisy document images},
	journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
	volume = {26},
	year = {2004},
	month = {2004/03//},
	pages = {337 - 353},
	abstract = {In this paper, we address the problem of the identification of text in noisy document images. We are especially focused on segmenting and identifying between handwriting and machine printed text because: 1) Handwriting in a document often indicates corrections, additions, or other supplemental information that should be treated differently from the main content and 2) the segmentation and recognition techniques requested for machine printed and handwritten text are significantly different. A novel aspect of our approach is that we treat noise as a separate class and model noise based on selected features. Trained Fisher classifiers are used to identify machine printed text and handwriting from noise and we further exploit context to refine the classification. A Markov Random Field-based (MRF) approach is used to model the geometrical structure of the printed text, handwriting, and noise to rectify misclassifications. Experimental results show that our approach is robust and can significantly improve page segmentation in noisy document collections.},
	keywords = {Automated;Reading;Reproducibility of Results;Sensitivity and Specificity;Signal Processing, Computer-Assisted;Information Storage and Retrieval;Models, Computer-Assisted;Pattern Recognition, Computer-Assisted;Stochastic Processes;Subtraction Technique;User-Computer Interface;Writing;, Markov random field;fisher classifiers;handwriting identification;machine printed text;noisy document images;page segmentation;recognition techniques;text identification;Markov processes;document image processing;feature extraction;handwriting recognition, Statistical;Numerical Analysis},
	isbn = {0162-8828},
	doi = {10.1109/TPAMI.2004.1262324},
	author = {Yefeng Zheng and Huiping Li and David Doermann}
}
