@conference {18155,
	title = {A pattern classification framework for theoretical analysis of component forensics},
	booktitle = {Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on},
	year = {2008},
	month = {2008/04/31/4},
	pages = {1665 - 1668},
	abstract = {Component forensics is an emerging methodology for forensic analysis that aims at estimating the algorithms and parameters in each component of a digital device. This paper proposes a theoretical foundation to examine the performance limits of component forensics. Using ideas from pattern classification theory, we define formal notions of identifiability of components in the information processing chain. We show that the parameters of certain device components can be accurately identified only in controlled settings through semi non-intrusive forensics, while the parameters of some others can be computed directly from the available sample data via complete non-intrusive analysis. We then extend the proposed theoretical framework to quantify and improve the accuracies and confidence in component parameter identification for several forensic applications.},
	keywords = {analysis;information, classification;, classification;parameter, component, estimation;pattern, forensics;component, forensics;pattern, identification;forensic, parameter, processing;nonintrusive},
	doi = {10.1109/ICASSP.2008.4517947},
	author = {Swaminathan,A. and M. Wu and Liu,K. J.R}
}
@conference {14388,
	title = {Combining Collective Classification and Link Prediction},
	booktitle = {Data Mining Workshops, 2007. ICDM Workshops 2007. Seventh IEEE International Conference on},
	year = {2007},
	month = {2007/10//},
	pages = {381 - 386},
	abstract = {The problems of object classification (labeling the nodes of a graph) and link prediction (predicting the links in a graph) have been largely studied independently. Commonly, object classification is performed assuming a complete set of known links and link prediction is done assuming a fully observed set of node attributes. In most real world domains, however, attributes and links are often missing or incorrect. Object classification is not provided with all the links relevant to correct classification and link prediction is not provided all the labels needed for accurate link prediction. In this paper, we propose an approach that addresses these two problems by interleaving object classification and link prediction in a collective algorithm. We investigate empirically the conditions under which an integrated approach to object classification and link prediction improves performance, and find that performance improves over a wide range of network types, and algorithm settings.},
	keywords = {algorithm;link, classification;, classification;graph, collective, prediction;object, theory;pattern},
	doi = {10.1109/ICDMW.2007.35},
	author = {Bilgic,M. and Namata,G.M. and Getoor, Lise}
}
@article {12603,
	title = {Face Verification Across Age Progression},
	journal = {Image Processing, IEEE Transactions on},
	volume = {15},
	year = {2006},
	month = {2006/11//},
	pages = {3349 - 3361},
	abstract = {Human faces undergo considerable amounts of variations with aging. While face recognition systems have been proven to be sensitive to factors such as illumination and pose, their sensitivity to facial aging effects is yet to be studied. How does age progression affect the similarity between a pair of face images of an individual? What is the confidence associated with establishing the identity between a pair of age separated face images? In this paper, we develop a Bayesian age difference classifier that classifies face images of individuals based on age differences and performs face verification across age progression. Further, we study the similarity of faces across age progression. Since age separated face images invariably differ in illumination and pose, we propose preprocessing methods for minimizing such variations. Experimental results using a database comprising of pairs of face images that were retrieved from the passports of 465 individuals are presented. The verification system for faces separated by as many as nine years, attains an equal error rate of 8.5\%},
	keywords = {age, Aging, Bayesian, classification;, classification;face, classifier;age, difference, effects;preprocessing, Face, image, images;error, methods;Bayes, methods;error, progression;age, rate;face, recognition, recognition;image, separated, statistics;face, systems;face, verification;facial},
	isbn = {1057-7149},
	doi = {10.1109/TIP.2006.881993},
	author = {Ramanathan,N. and Chellapa, Rama}
}
@conference {13192,
	title = {Pedestrian classification from moving platforms using cyclic motion pattern},
	booktitle = {Image Processing, 2005. ICIP 2005. IEEE International Conference on},
	volume = {2},
	year = {2005},
	month = {2005/09//},
	pages = {II - 854-7 - II - 854-7},
	abstract = {This paper describes an efficient pedestrian detection system for videos acquired from moving platforms. Given a detected and tracked object as a sequence of images within a bounding box, we describe the periodic signature of its motion pattern using a twin-pendulum model. Then a principle gait angle is extracted in every frame providing gait phase information. By estimating the periodicity from the phase data using a digital phase locked loop (dPLL), we quantify the cyclic pattern of the object, which helps us to continuously classify it as a pedestrian. Past approaches have used shape detectors applied to a single image or classifiers based on human body pixel oscillations, but ours is the first to integrate a global cyclic motion model and periodicity analysis. Novel contributions of this paper include: i) development of a compact shape representation of cyclic motion as a signature for a pedestrian, ii) estimation of gait period via a feedback loop module, and iii) implementation of a fast online pedestrian classification system which operates on videos acquired from moving platforms.},
	keywords = {analysis;, angle;, body, classification;, compact, cyclic, DETECTION, detection;, digital, Feedback, Gait, human, image, information;, locked, loop, loop;, loops;, module;, MOTION, object, oscillations;, pattern;, pedestrian, phase, Pixel, principle, representation;, sequence;, sequences;, SHAPE, system;},
	doi = {10.1109/ICIP.2005.1530190},
	author = {Yang Ran and Qinfen Zheng and Weiss, I. and Davis, Larry S. and Abd-Almageed, Wael and Liang Zhao}
}
@conference {14845,
	title = {Using the inner-distance for classification of articulated shapes},
	booktitle = {Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on},
	volume = {2},
	year = {2005},
	month = {2005/06//},
	pages = {719 - 726 vol. 2 - 719 - 726 vol. 2},
	abstract = {We propose using the inner-distance between landmark points to build shape descriptors. The inner-distance is defined as the length of the shortest path between landmark points within the shape silhouette. We show that the inner-distance is articulation insensitive and more effective at capturing complex shapes with part structures than Euclidean distance. To demonstrate this idea, it is used to build a new shape descriptor based on shape contexts. After that, we design a dynamic programming based method for shape matching and comparison. We have tested our approach on a variety of shape databases including an articulated shape dataset, MPEG7 CE-Shape-1, Kimia silhouettes, a Swedish leaf database and a human motion silhouette dataset. In all the experiments, our method demonstrates effective performance compared with other algorithms.},
	keywords = {articulated, CE-Shape-1, classification;, database;, databases;, dataset;, descriptor;, dynamic, human, image, inner-distance;, Kimia, landmark, leaf, matching;, MOTION, MPEG7, points;, programming;, SHAPE, silhouette, silhouette;, Swedish, visual},
	doi = {10.1109/CVPR.2005.362},
	author = {Ling,H. and Jacobs, David W.}
}
@conference {12693,
	title = {Classification probability analysis of principal component space analysis},
	booktitle = {Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on},
	volume = {1},
	year = {2004},
	month = {2004/08//},
	pages = {240 - 243 Vol.1 - 240 - 243 Vol.1},
	abstract = {In a previous paper, we have presented a new linear classification algorithm, principal component space analysis (PCNSA) which is designed for problems like object recognition where different classes have unequal and non-white noise covariance matrices. PCNSA first obtains a principal components space (PCA space) for the entire data and in this PCA space, it finds for each class "i", an M<sub>i</sub> dimensional subspace along which the class{\textquoteright}s intra-class 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}s mean in the class{\textquoteright}s ANS is a minimum. In this paper, we discuss the PCNSA algorithm more precisely and derive tight upper bounds on its classification error probability. We use these expressions to compare classification performance of PCNSA with that of subspace linear discriminant analysis (SLDA).},
	keywords = {algorithm;, analysis;, approximate, classification, classification;, component, covariance, discriminant, error, intraclass, linear, matrices;, matrix;, noise;, nonwhite, object, pattern, PCA;, principal, probability;, recognition;, space, space;, statistics;, subspace, variance;},
	doi = {10.1109/ICPR.2004.1334068},
	author = {Vaswani, N. and Chellapa, Rama}
}
@conference {13625,
	title = {Product approximation by minimizing the upper bound of Bayes error rate for Bayesian combination of classifiers},
	booktitle = {Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on},
	volume = {1},
	year = {2004},
	month = {2004/08//},
	pages = {252 - 255 Vol.1 - 252 - 255 Vol.1},
	abstract = {In combining multiple classifiers using a Bayesian formalism, a high dimensional probability distribution is composed of a class and decisions of classifiers. In order to do product approximation of the probability distribution, the upper bound of Bayes error rate, bounded by the conditional entropy of a class and decisions, should be minimized. A second-order dependency-based product approximation is proposed in this paper by considering the second-order dependency between the class and decisions. The proposed method is evaluated by combining the classifiers recognizing unconstrained handwritten numerals.},
	keywords = {approximation;, Bayes, Bayesian, bound, character, classification;, classifiers;, conditional, distribution;, entropy;, error, formalism;, handwritten, methods;, minimisation;, multiple, numerals;, pattern, probability, probability;, Product, rate;, recognition;, statistics;, unconstrained, upper},
	doi = {10.1109/ICPR.2004.1334071},
	author = {Kang,Hee-Joong and David Doermann}
}
@conference {12696,
	title = {Role of shape and kinematics in human movement analysis},
	booktitle = {Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on},
	volume = {1},
	year = {2004},
	month = {2004/07/02/june},
	pages = {I-730 - I-737 Vol.1 - I-730 - I-737 Vol.1},
	abstract = {Human gait and activity analysis from video is presently attracting a lot of attention in the computer vision community. In this paper we analyze the role of two of the most important cues in human motion-shape and kinematics. We present an experimental framework whereby it is possible to evaluate the relative importance of these two cues in computer vision based recognition algorithms. In the process, we propose a new gait recognition algorithm by computing the distance between two sequences of shapes that lie on a spherical manifold. In our experiments, shape is represented using Kendall{\textquoteright}s definition of shape. Kinematics is represented using a Linear Dynamical system We place particular emphasis on human gait. Our conclusions show that shape plays a role which is more significant than kinematics in current automated gait based human identification algorithms. As a natural extension we study the role of shape and kinematics in activity recognition. Our experiments indicate that we require models that contain both shape and kinematics in order to perform accurate activity classification. These conclusions also allow us to explain the relative performance of many existing methods in computer-based human activity modeling.},
	keywords = {activity, algorithm;, algorithms;, analysis;, autoregressive, average, based, classification;, community;, Computer, definition;, dynamical, extraction;, feature, Gait, hidden, human, identification, image, Kendall, linear, manifold;, Markov, modeling;, models;, MOTION, Movement, moving, processes;, recognition, sequences;, SHAPE, spherical, system;, VISION, vision;},
	doi = {10.1109/CVPR.2004.1315104},
	author = {Veeraraghavan,A. and Chowdhury, A.R. and Chellapa, Rama}
}
@conference {18172,
	title = {Classification-based spatial error concealment for images},
	booktitle = {Image Processing, 2003. ICIP 2003. Proceedings. 2003 International Conference on},
	volume = {2},
	year = {2003},
	month = {2003/09//},
	pages = {II - 675-8 vol.3 - II - 675-8 vol.3},
	abstract = {This paper presents a new, classification-based spatial error concealment algorithm for images. The proposed scheme takes advantage of two state-of-the-art concealment schemes and adaptively selects a better suitable concealment scheme for each corrupted block. Using a support vector machine (SVM) classifier, our proposed approach outperforms the prior art in terms of the concealment quality and has moderate computational complexity.},
	keywords = {algorithm;, classification-based, classification;, classifier;, complexity;, computational, concealment, error, image, Machine;, machines;, moderate, scheme;, spatial, state-of-the-art, Support, SVM, vector},
	doi = {10.1109/ICIP.2003.1246770},
	author = {Chen,M. and Wu,M. and Zheng,Y.}
}
@conference {13243,
	title = {Human body pose estimation using silhouette shape analysis},
	booktitle = {Proceedings. IEEE Conference on Advanced Video and Signal Based Surveillance, 2003.},
	year = {2003},
	month = {2003/07//},
	pages = {263 - 270},
	abstract = {We describe a system for human body pose estimation from multiple views that is fast and completely automatic. The algorithm works in the presence of multiple people by decoupling the problems of pose estimation of different people. The pose is estimated based on a likelihood function that integrates information from multiple views and thus obtains a globally optimal solution. Other characteristics that make our method more general than previous work include: (1) no manual initialization; (2) no specification of the dimensions of the 3D structure; (3) no reliance on some learned poses or patterns of activity; (4) insensitivity to edges and clutter in the background and within the foreground. The algorithm has applications in surveillance and promising results have been obtained.},
	keywords = {3D, analysis;, body, classification;, clutter;, detection;, estimation;, extraction;, feature, function;, human, image, likelihood, multiple, object, parameter, parameters;, Pixel, pose, probability;, segmentation;, segmentations;, SHAPE, silhouette, structure;, surveillance;, views;},
	doi = {10.1109/AVSS.2003.1217930},
	author = {Mittal,A. and Liang Zhao and Davis, Larry S.}
}
@conference {13239,
	title = {Mean-shift analysis using quasiNewton methods},
	booktitle = {Image Processing, 2003. ICIP 2003. Proceedings. 2003 International Conference on},
	volume = {2},
	year = {2003},
	month = {2003/09//},
	pages = {II - 447-50 vol.3 - II - 447-50 vol.3},
	abstract = {Mean-shift analysis is a general nonparametric clustering technique based on density estimation for the analysis of complex feature spaces. The algorithm consists of a simple iterative procedure that shifts each of the feature points to the nearest stationary point along the gradient directions of the estimated density function. It has been successfully applied to many applications such as segmentation and tracking. However, despite its promising performance, there are applications for which the algorithm converges too slowly to be practical. We propose and implement an improved version of the mean-shift algorithm using quasiNewton methods to achieve higher convergence rates. Another benefit of our algorithm is its ability to achieve clustering even for very complex and irregular feature-space topography. Experimental results demonstrate the efficiency and effectiveness of our algorithm.},
	keywords = {analysis;, classification;, clustering, clustering;, Convergence, density, estimation;, feature-space, image, irregular, iterative, Mean-shift, method;, methods;, Newton, nonparametric, pattern, procedure;, quasiNewton, rates;, segmentation;, technique;, topography;},
	doi = {10.1109/ICIP.2003.1246713},
	author = {Yang,C. and Duraiswami, Ramani and DeMenthon,D. and Davis, Larry S.}
}
@conference {12733,
	title = {Statistical shape theory for activity modeling},
	booktitle = {Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP {\textquoteright}03). 2003 IEEE International Conference on},
	volume = {3},
	year = {2003},
	month = {2003/04//},
	pages = {III - 493-6 vol.3 - III - 493-6 vol.3},
	abstract = {Monitoring activities in a certain region from video data is an important surveillance problem. The goal is to learn the pattern of normal activities and detect unusual ones by identifying activities that deviate appreciably from the typical ones. We propose an approach using statistical shape theory based on the shape model of D.G. Kendall et al. (see "Shape and Shape Theory", John Wiley and Sons, 1999). In a low resolution video, each moving object is best represented as a moving point mass or particle. In this case, an activity can be defined by the interactions of all or some of these moving particles over time. We model this configuration of the particles by a polygonal shape formed from the locations of the points in a frame and the activity by the deformation of the polygons in time. These parameters are learned for each typical activity. Given a test video sequence, an activity is classified as abnormal if the probability for the sequence (represented by the mean shape and the dynamics of the deviations), given the model, is below a certain threshold The approach gives very encouraging results in surveillance applications using a single camera and is able to identify various kinds of abnormal behavior.},
	keywords = {abnormal, activities, activity, analysis;, behavior;, classification;, data;, image, mass;, matching;, modeling;, monitoring;, moving, normal, particle;, pattern, pattern;, point, polygonal, probability;, problem;, processing;, sequence;, sequences;, SHAPE, shape;, signal, statistical, Surveillance, surveillance;, theory;, video},
	doi = {10.1109/ICASSP.2003.1199519},
	author = {Vaswani, N. and Chowdhury, A.R. and Chellapa, Rama}
}
@conference {12763,
	title = {An experimental evaluation of linear and kernel-based methods for face recognition},
	booktitle = {Applications of Computer Vision, 2002. (WACV 2002). Proceedings. Sixth IEEE Workshop on},
	year = {2002},
	month = {2002///},
	pages = {13 - 18},
	abstract = {In this paper we present the results of a comparative study of linear and kernel-based methods for face recognition. The methods used for dimensionality reduction are Principal Component Analysis (PCA), Kernel Principal Component Analysis (KPCA), Linear Discriminant Analysis (LDA) and Kernel Discriminant Analysis (KDA). The methods used for classification are Nearest Neighbor (NN) and Support Vector Machine (SVM). In addition, these classification methods are applied on raw images to gauge the performance of these dimensionality reduction techniques. All experiments have been performed on images from UMIST Face Database.},
	keywords = {analysis;, classification;, component, discriminant, Face, image, Kernel, linear, Machine;, nearest, neighbor;, principal, recognition;, Support, vector},
	doi = {10.1109/ACV.2002.1182137},
	author = {Gupta, H. and Agrawala, Ashok K. and Pruthi, T. and Shekhar, C. and Chellapa, Rama}
}
@conference {18400,
	title = {Maintaining software with a security perspective},
	booktitle = {Software Maintenance, 2002. Proceedings. International Conference on},
	year = {2002},
	month = {2002///},
	pages = {194 - 203},
	abstract = {Testing for software security is a lengthy, complex and costly process. Currently, security testing is done using penetration analysis and formal verification of security kernels. These methods are not complete and are difficult to use. Hence it is essential to focus testing effort in areas that have a greater number of security vulnerabilities to develop secure software as well as meet budget and time constraints. We propose a testing strategy based on a classification of vulnerabilities to develop secure and stable systems. This taxonomy will enable a system testing and maintenance group to understand the distribution of security vulnerabilities and prioritize their testing effort according to the impact the vulnerabilities have on the system. This is based on Landwehr{\textquoteright}s (1994) classification scheme for security flaws and we evaluated it using a database of 1360 operating system vulnerabilities. This analysis indicates vulnerabilities tend to be focused in relatively few areas and associated with a small number of software engineering issues.},
	keywords = {(computers);, budget, classification, classification;, constraints;, data;, engineering;, flaw, maintenance;, of, operating, program, scheme;, Security, software, stable, system, systems, systems;, testing;, TIME, vulnerabilities;, vulnerability},
	doi = {10.1109/ICSM.2002.1167766},
	author = {Jiwnani,K. and Zelkowitz, Marvin V}
}
@conference {13618,
	title = {Page classification through logical labelling},
	booktitle = {Pattern Recognition, 2002. Proceedings. 16th International Conference on},
	volume = {3},
	year = {2002},
	month = {2002///},
	pages = {477 - 480 vol.3 - 477 - 480 vol.3},
	abstract = {We propose an integrated approach to page classification and logical labelling. Layout is represented by a fully connected attributed relational graph that is matched to the graph of an unknown document, achieving classification and labelling simultaneously. By incorporating global constraints in an integrated fashion, ambiguity at the zone level can be reduced, providing robustness to noise and variation. Models are automatically trained from sample documents. Experimental results show promise for the classification and labelling of technical article title pages, and supports the idea of a hierarchical model base.},
	keywords = {article, attributed, base;, character, classification;, constraints;, document, document;, experimental, global, graph, graph;, hierarchical, image, images;, labelling;, logical, model, noise;, OCR;, optical, page, pages;, processing;, recognition;, relational, results;, technical, theory;, title, unknown},
	doi = {10.1109/ICPR.2002.1047980},
	author = {Liang,Jian and David Doermann and Ma,M. and Guo,J. K}
}
@conference {18373,
	title = {An improved classification tree analysis of high cost modules based upon an axiomatic definition of complexity},
	booktitle = {Software Reliability Engineering, 1992. Proceedings., Third International Symposium on},
	year = {1992},
	month = {1992/10//},
	pages = {164 - 172},
	abstract = {Identification of high cost modules has been viewed as one mechanism to improve overall system reliability, since such modules tend to produce more than their fair share of problems. A decision tree model has previously been used to identify such modules. In this paper, a previously developed axiomatic model of program complexity is merged with the previously developed decision tree process for an improvement in the ability to identify such modules. This improvement has been tested using data from the NASA Software Engineering Laboratory},
	keywords = {(mathematics);, analysis;, axiomatic, classification, classification;, complexity;, computational, cost, decision, definition;, high, metrics;, model;, modules;, overall, program, reliability;, software, subroutines;, system, tree, TREES},
	doi = {10.1109/ISSRE.1992.285848},
	author = {Tian,Jianhui and Porter, Adam and Zelkowitz, Marvin V}
}
