@article {12552,
	title = {Algorithmic and Architectural Optimizations for Computationally Efficient Particle Filtering},
	journal = {Image Processing, IEEE Transactions on},
	volume = {17},
	year = {2008},
	month = {2008/05//},
	pages = {737 - 748},
	abstract = {In this paper, we analyze the computational challenges in implementing particle filtering, especially to video sequences. Particle filtering is a technique used for filtering nonlinear dynamical systems driven by non-Gaussian noise processes. It has found widespread applications in detection, navigation, and tracking problems. Although, in general, particle filtering methods yield improved results, it is difficult to achieve real time performance. In this paper, we analyze the computational drawbacks of traditional particle filtering algorithms, and present a method for implementing the particle filter using the Independent Metropolis Hastings sampler, that is highly amenable to pipelined implementations and parallelization. We analyze the implementations of the proposed algorithm, and, in particular, concentrate on implementations that have minimum processing times. It is shown that the design parameters for the fastest implementation can be chosen by solving a set of convex programs. The proposed computational methodology was verified using a cluster of PCs for the application of visual tracking. We demonstrate a linear speedup of the algorithm using the methodology proposed in the paper.},
	keywords = {Automated;Reproducibility of Results;Sensitivity and Specificity;Signal Processing, Computer-Assisted;Models, Computer-Assisted;Video Recording;, convex program;independent Metropolis Hastings sampler;nonGaussian noise process;nonlinear dynamical system filtering;particle filtering algorithm;pipelined architectural optimization;video sequences;visual tracking;convex programming;image sequences;opti, Statistical;Pattern Recognition},
	isbn = {1057-7149},
	doi = {10.1109/TIP.2008.920760},
	author = {Sankaranarayanan,A. C and Srivastava, A. and Chellapa, Rama}
}
@article {13977,
	title = {A Fast Algorithm for Learning a Ranking Function from Large-Scale Data Sets},
	journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
	volume = {30},
	year = {2008},
	month = {2008/07//},
	pages = {1158 - 1170},
	abstract = {We consider the problem of learning a ranking function that maximizes a generalization of the Wilcoxon-Mann-Whitney statistic on the training data. Relying on an e-accurate approximation for the error function, we reduce the computational complexity of each iteration of a conjugate gradient algorithm for learning ranking functions from O(m<sup>2</sup>) to O(m), where m is the number of training samples. Experiments on public benchmarks for ordinal regression and collaborative filtering indicate that the proposed algorithm is as accurate as the best available methods in terms of ranking accuracy, when the algorithms are trained on the same data. However, since it is several orders of magnitude faster than the current state-of-the-art approaches, it is able to leverage much larger training data sets.},
	keywords = {Automated;, Factual;Information Storage and Retrieval;Likelihood Functions;Models, Statistical;Pattern Recognition, Wilcoxon-Mann-Whitney statistics;collaborative filtering;error function;gradient algorithm;large-scale data sets;learning ranking functions;ranking function;training data;computational complexity;error analysis;learning (artificial intelligence);regressio},
	isbn = {0162-8828},
	doi = {10.1109/TPAMI.2007.70776},
	author = {Raykar,V.C. and Duraiswami, Ramani and Krishnapuram,B.}
}
@article {12613,
	title = {From sample similarity to ensemble similarity: probabilistic distance measures in reproducing kernel Hilbert space},
	journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
	volume = {28},
	year = {2006},
	month = {2006/06//},
	pages = {917 - 929},
	abstract = {This paper addresses the problem of characterizing ensemble similarity from sample similarity in a principled manner. Using a reproducing kernel as a characterization of sample similarity, we suggest a probabilistic distance measure in the reproducing kernel Hilbert space (RKHS) as the ensemble similarity. Assuming normality in the RKHS, we derive analytic expressions for probabilistic distance measures that are commonly used in many applications, such as Chernoff distance (or the Bhattacharyya distance as its special case), Kullback-Leibler divergence, etc. Since the reproducing kernel implicitly embeds a nonlinear mapping, our approach presents a new way to study these distances whose feasibility and efficiency is demonstrated using experiments with synthetic and real examples. Further, we extend the ensemble similarity to the reproducing kernel for ensemble and study the ensemble similarity for more general data representations.},
	keywords = {Automated;Sample Size;Signal Processing, Bhattacharyya distance;Chernoff distance;Gram matrix;Kullback-Leibler divergence;data representations;ensemble similarity characterization;nonlinear mapping;probabilistic distance measures;reproducing kernel Hilbert space;sample similarity characterizatio, Computer-Assisted;, Statistical;Pattern Recognition},
	isbn = {0162-8828},
	doi = {10.1109/TPAMI.2006.120},
	author = {Zhou,S. K and Chellapa, Rama}
}
@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 {12654,
	title = {Statistical bias in 3-D reconstruction from a monocular video},
	journal = {Image Processing, IEEE Transactions on},
	volume = {14},
	year = {2005},
	month = {2005/08//},
	pages = {1057 - 1062},
	abstract = {The present state-of-the-art in computing the error statistics in three-dimensional (3-D) reconstruction from video concentrates on estimating the error covariance. A different source of error which has not received much attention is the fact that the reconstruction estimates are often significantly statistically biased. In this paper, we derive a precise expression for the bias in the depth estimate, based on the continuous (differentiable) version of structure from motion (SfM). Many SfM algorithms, or certain portions of them, can be posed in a linear least-squares (LS) framework Ax=b. Examples include initialization procedures for bundle adjustment or algorithms that alternately estimate depth and camera motion. It is a well-known fact that the LS estimate is biased if the system matrix A is noisy. In SfM, the matrix A contains point correspondences, which are always difficult to obtain precisely; thus, it is expected that the structure and motion estimates in such a formulation of the problem would be biased. Existing results on the minimum achievable variance of the SfM estimator are extended by deriving a generalized Cramer-Rao lower bound. A detailed analysis of the effect of various camera motion parameters on the bias is presented. We conclude by presenting the effect of bias compensation on reconstructing 3-D face models from rendered images.},
	keywords = {3D face models, 3D video reconstruction, algorithms, artifacts, Artificial intelligence, Automated;Signal Processing, bias compensation, bundle adjustment, camera motion estimation, Computer simulation, Computer-Assisted;Imaging, Computer-Assisted;Subtraction Technique;Video Recording;, depth estimate, error covariance estimation, error statistics, generalized Cramer-Rao lower bound, Image Enhancement, Image Interpretation, Image reconstruction, initialization procedures, least squares approximations, linear least-squares framework, monocular video, motion compensation, Motion estimation, statistical bias, Statistical;Pattern Recognition, structure from motion algorithms, Three-Dimensional;Information Storage and Retrieval;Models, video signal processing},
	isbn = {1057-7149},
	doi = {10.1109/TIP.2005.849775},
	author = {Roy-Chowdhury, A.K. and Chellapa, Rama}
}
@article {12690,
	title = {Identification of humans using gait},
	journal = {Image Processing, IEEE Transactions on},
	volume = {13},
	year = {2004},
	month = {2004/09//},
	pages = {1163 - 1173},
	abstract = {We propose a view-based approach to recognize humans from their gait. Two different image features have been considered: the width of the outer contour of the binarized silhouette of the walking person and the entire binary silhouette itself. To obtain the observation vector from the image features, we employ two different methods. In the first method, referred to as the indirect approach, the high-dimensional image feature is transformed to a lower dimensional space by generating what we call the frame to exemplar (FED) distance. The FED vector captures both structural and dynamic traits of each individual. For compact and effective gait representation and recognition, the gait information in the FED vector sequences is captured in a hidden Markov model (HMM). In the second method, referred to as the direct approach, we work with the feature vector directly (as opposed to computing the FED) and train an HMM. We estimate the HMM parameters (specifically the observation probability B) based on the distance between the exemplars and the image features. In this way, we avoid learning high-dimensional probability density functions. The statistical nature of the HMM lends overall robustness to representation and recognition. The performance of the methods is illustrated using several databases.},
	keywords = {Automated;Reproducibility of Results;Sensitivity and Specificity;Signal Processing, binary silhouette;frame to exemplar distance;gait databases;gait recognition;hidden Markov model;human identification;image features;observation probability;observation vector;hidden Markov models;image recognition;image representation;image sequences;pro, Biological;Models, Computer-Assisted;Models, Computer-Assisted;Subtraction Technique;Task Performance and Analysis;Video Recording;, Statistical;Pattern Recognition},
	isbn = {1057-7149},
	doi = {10.1109/TIP.2004.832865},
	author = {Kale, A. and Sundaresan, A. and Rajagopalan, AN and Cuntoor, N.P. and Roy-Chowdhury, A.K. and Kruger, V. and Chellapa, Rama}
}
