@article {18242,
	title = {Modeling and Analysis of Correlated Binary Fingerprints for Content Identification},
	journal = {Information Forensics and Security, IEEE Transactions on},
	volume = {6},
	year = {2011},
	month = {2011/09//},
	pages = {1146 - 1159},
	abstract = {Multimedia identification via content fingerprints is used in many applications, such as content filtering on user-generated content websites, and automatic multimedia identification and tagging. A compact $\#$x201C;fingerprint $\#$x201D; is computed for each multimedia signal that captures robust and unique properties of the perceptual content, which is later used for identifying the multimedia. Several different multimedia fingerprinting schemes have been proposed in the literature and have been evaluated through experiments. To complement these experimental evaluations and provide guidelines for choosing system parameters and designing better schemes, this paper develops models for content fingerprinting and provides an analysis of the identification performance under these models. As a first step, bounds on the identification accuracy and the required fingerprint length for the simplest case when the fingerprint bits are modeled as i.i.d. are summarized. Markov Random Fields are then used to address more realistic settings of fingerprints with correlated components. The optimal likelihood ratio detector is derived and a statistical physics inspired approach for computing the probability of detection and probability of false alarm is described. The analysis shows that the commonly used Hamming distance detection criterion is susceptible to correlations among fingerprint bits, whereas the optimal log-likelihood ratio decision rule yields 5-20\% improvement in the accuracy over a range of correlations. Simulation results demonstrate the validity of the theoretical predictions.},
	keywords = {alarm, approach;user, binary, computing;, content, criterion;Markov, decision, DETECTION, detector;statistical, distance, fields;content, filtering;content, filtering;multimedia, fingerprinting, fingerprints;detection, generated, Hamming, identification;correlated, identification;optimal, inspired, likelihood, Physics, probability;false, probability;log, processes;Web, random, ratio, rule;multimedia, schemes;multimedia, sites;information, Websites;Markov},
	isbn = {1556-6013},
	doi = {10.1109/TIFS.2011.2152394},
	author = {Varna,A.L. and M. Wu}
}
@conference {13095,
	title = {Combining multiple kernels for efficient image classification},
	booktitle = {Applications of Computer Vision (WACV), 2009 Workshop on},
	year = {2009},
	month = {2009/12//},
	pages = {1 - 8},
	abstract = {We investigate the problem of combining multiple feature channels for the purpose of efficient image classification. Discriminative kernel based methods, such as SVMs, have been shown to be quite effective for image classification. To use these methods with several feature channels, one needs to combine base kernels computed from them. Multiple kernel learning is an effective method for combining the base kernels. However, the cost of computing the kernel similarities of a test image with each of the support vectors for all feature channels is extremely high. We propose an alternate method, where training data instances are selected, using AdaBoost, for each of the base kernels. A composite decision function, which can be evaluated by computing kernel similarities with respect to only these chosen instances, is learnt. This method significantly reduces the number of kernel computations required during testing. Experimental results on the benchmark UCI datasets, as well as on a challenging painting dataset, are included to demonstrate the effectiveness of our method.},
	keywords = {(artificial, AdaBoost;base, channels;multiple, classification;kernel, classification;learning, decision, feature, function;discriminative, intelligence);support, Kernel, kernel;image, kernels;composite, learning;support, machine;image, machines;, similarity;multiple, vector},
	doi = {10.1109/WACV.2009.5403040},
	author = {Siddiquie,B. and Vitaladevuni,S.N. and Davis, Larry S.}
}
@conference {18244,
	title = {Modeling content fingerprints using markov random fields},
	booktitle = {Information Forensics and Security, 2009. WIFS 2009. First IEEE International Workshop on},
	year = {2009},
	month = {2009/12//},
	pages = {111 - 115},
	abstract = {Content fingerprints are widely employed for identifying multimedia in various applications. A {\^A}{\textquestiondown}fingerprint{\^A}{\textquestiondown} of a video or audio is a short signature that captures unique characteristics of the signal and can be used to perform robust identification. Several fingerprinting techniques have been proposed in the literature and are often evaluated using benchmark databases. To complement these experimental evaluations, this paper develops a theoretical model for content fingerprints and evaluates the identification accuracy. Fingerprints and the noise are modeled as Markov random fields and the optimal decision rule for matching is derived. An algorithm to compute the probability of correct detection and the false alarm rate by estimating the density of states is described. Numerical results are provided for a model of a block based binary fingerprinting scheme and the influence of the fingerprint correlation and the noise on the detection accuracy is studied.},
	keywords = {based, binary, correlation;multimedia;noise;optimal, data;, decision, field;block, fingerprint;fingerprint, fingerprinting;content, Markov, of, processes;multimedia, random, rule;Markov, systems;probability;security},
	doi = {10.1109/WIFS.2009.5386471},
	author = {Varna,A.L. and M. Wu}
}
@conference {12582,
	title = {In Situ Evaluation of Tracking Algorithms Using Time Reversed Chains},
	booktitle = {Computer Vision and Pattern Recognition, 2007. CVPR {\textquoteright}07. IEEE Conference on},
	year = {2007},
	month = {2007/06//},
	pages = {1 - 8},
	abstract = {Automatic evaluation of visual tracking algorithms in the absence of ground truth is a very challenging and important problem. In the context of online appearance modeling, there is an additional ambiguity involving the correctness of the appearance model. In this paper, we propose a novel performance evaluation strategy for tracking systems based on particle filter using a time reversed Markov chain. Starting from the latest observation, the time reversed chain is propagated back till the starting time t = 0 of the tracking algorithm. The posterior density of the time reversed chain is also computed. The distance between the posterior density of the time reversed chain (at t = 0) and the prior density used to initialize the tracking algorithm forms the decision statistic for evaluation. It is postulated that when the data is generated true to the underlying models, the decision statistic takes a low value. We empirically demonstrate the performance of the algorithm against various common failure modes in the generic visual tracking problem. Finally, we derive a small frame approximation that allows for very efficient computation of the decision statistic.},
	keywords = {(numerical, algorithm;Markov, chain;tracking, decision, density;time, detection;particle, Evaluation, evaluation;object, filter;performance, Filtering, Markov, methods);tracking;visual, processes;decision, reversed, servoing;, situ, statistics;in, strategy;posterior, systems;visual, theory;object, tracking, tracking;particle},
	doi = {10.1109/CVPR.2007.382992},
	author = {Wu,Hao and Sankaranarayanan,A. C and Chellapa, Rama}
}
@conference {12704,
	title = {Fusion of gait and face for human identification},
	booktitle = {Acoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP {\textquoteright}04). IEEE International Conference on},
	volume = {5},
	year = {2004},
	month = {2004/05//},
	pages = {V - 901-4 vol.5 - V - 901-4 vol.5},
	abstract = {Identification of humans from arbitrary view points is an important requirement for different tasks including perceptual interfaces for intelligent environments, covert security and access control etc. For optimal performance, the system must use as many cues as possible and combine them in meaningful ways. In this paper, we discuss fusion of face and gait cues for the single camera case. We present a view invariant gait recognition algorithm for gait recognition. We employ decision fusion to combine the results of our gait recognition algorithm and a face recognition algorithm based on sequential importance sampling. We consider two fusion scenarios: hierarchical and holistic. The first involves using the gait recognition algorithm as a filter to pass on a smaller set of candidates to the face recognition algorithm. The second involves combining the similarity scores obtained individually from the face and gait recognition algorithms. Simple rules like the SUM, MIN and PRODUCT are used for combining the scores. The results of fusion experiments are demonstrated on the NIST database which has outdoor gait and face data of 30 subjects.},
	keywords = {access, algorithm;, analysis;, combining, control;, covert, cues;, data, decision, Environment, Face, fusion;, Gait, hierarchical, holistic, human, identification;, importance, intelligent, interfaces;, invariant, perceptual, recognition, recognition;, rules;, sampling;, score, scores;, security;, sensor, sequential, similarity, view},
	doi = {10.1109/ICASSP.2004.1327257},
	author = {Kale, A. and Roy Chowdhury, A.K. and Chellapa, Rama}
}
@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}
}
@article {18362,
	title = {A management tool for evaluation of software design},
	journal = {Software Engineering, IEEE Transactions on},
	volume = {17},
	year = {1991},
	month = {1991/09//},
	pages = {961 - 971},
	abstract = {A model for evaluating software designs based on extending the functional model of program verification with concepts from economic decision theory has been proposed. The authors describe the method, and discuss a prototype implementation of a tool, called Selector, which implements this technique. It is suggested that a system like Selector can be used in two ways: as a decision support system for management to be used in the process of making choices among various alternatives; and as a prototyping investigative system for proposing and answering a series of {\textquoteleft}what if{\textquoteright} scenarios. The proposed model depends on a risk analysis, of each potential solution and aspects of decision theory to modify the evaluation strategy. The model depends on equilibrium probabilities for generating answers},
	keywords = {analysis;software, decision, designs;decision, investigative, model;management, probabilities;evaluation, prototyping;software, Selector;decision, strategy;functional, Support, system;economic, system;risk, systems;program, theory;equilibrium, tool;program, tools;, verification;prototyping, verification;software},
	isbn = {0098-5589},
	doi = {10.1109/32.92916},
	author = {Cardenas-Garcia,S. and Zelkowitz, Marvin V}
}
