@article {13065,
	title = {Density-Based Multifeature Background Subtraction with Support Vector Machine},
	journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
	volume = {34},
	year = {2012},
	month = {2012/05//},
	pages = {1017 - 1023},
	abstract = {Background modeling and subtraction is a natural technique for object detection in videos captured by a static camera, and also a critical preprocessing step in various high-level computer vision applications. However, there have not been many studies concerning useful features and binary segmentation algorithms for this problem. We propose a pixelwise background modeling and subtraction technique using multiple features, where generative and discriminative techniques are combined for classification. In our algorithm, color, gradient, and Haar-like features are integrated to handle spatio-temporal variations for each pixel. A pixelwise generative background model is obtained for each feature efficiently and effectively by Kernel Density Approximation (KDA). Background subtraction is performed in a discriminative manner using a Support Vector Machine (SVM) over background likelihood vectors for a set of features. The proposed algorithm is robust to shadow, illumination changes, spatial variations of background. We compare the performance of the algorithm with other density-based methods using several different feature combinations and modeling techniques, both quantitatively and qualitatively.},
	keywords = {algorithm;density-based, application;illumination, approximation;object, background, camera;support, change;kernel, Computer, density, detection;pixelwise, detection;support, extraction;image, feature;background, generative, Haar-like, likelihood, machine;Haar, machines;vectors;, modeling, multifeature, segmentation, segmentation;object, subtraction, technique;discriminative, technique;high-level, techniques;spatial, transforms;cameras;computer, variation;spatio-temporal, variation;static, vector, vector;binary, VISION, vision;feature},
	isbn = {0162-8828},
	doi = {10.1109/TPAMI.2011.243},
	author = {Han,Bohyung and Davis, Larry S.}
}
@conference {13071,
	title = {Action recognition using Partial Least Squares and Support Vector Machines},
	booktitle = {Image Processing (ICIP), 2011 18th IEEE International Conference on},
	year = {2011},
	month = {2011/09//},
	pages = {533 - 536},
	abstract = {We introduce an action recognition approach based on Partial Least Squares (PLS) and Support Vector Machines (SVM). We extract very high dimensional feature vectors representing spatio-temporal properties of actions and use multiple PLS regressors to find relevant features that distinguish amongst action classes. Finally, we use a multi-class SVM to learn and classify those relevant features. We applied our approach to INRIA{\textquoteright}s IXMAS dataset. Experimental results show that our method is superior to other methods applied to the IXMAS dataset.},
	keywords = {analysis;support, approach;multiclass, approximations;regression, dataset;action, dimensional, extraction;feature, extraction;image, feature, high, INRIA, IXMAS, least, machines;, machines;very, partial, properties;support, recognition, recognition;least, regressors;spatiotemporal, squares, SVM;multiple, vector, vectors},
	doi = {10.1109/ICIP.2011.6116399},
	author = {Ramadan,S. and Davis, Larry S.}
}
@article {14869,
	title = {Face Verification Across Age Progression Using Discriminative Methods},
	journal = {Information Forensics and Security, IEEE Transactions on},
	volume = {5},
	year = {2010},
	month = {2010/03//},
	pages = {82 - 91},
	abstract = {Face verification in the presence of age progression is an important problem that has not been widely addressed. In this paper, we study the problem by designing and evaluating discriminative approaches. These directly tackle verification tasks without explicit age modeling, which is a hard problem by itself. First, we find that the gradient orientation, after discarding magnitude information, provides a simple but effective representation for this problem. This representation is further improved when hierarchical information is used, which results in the use of the gradient orientation pyramid (GOP). When combined with a support vector machine GOP demonstrates excellent performance in all our experiments, in comparison with seven different approaches including two commercial systems. Our experiments are conducted on the FGnet dataset and two large passport datasets, one of them being the largest ever reported for recognition tasks. Second, taking advantage of these datasets, we empirically study how age gaps and related issues (including image quality, spectacles, and facial hair) affect recognition algorithms. We found surprisingly that the added difficulty of verification produced by age gaps becomes saturated after the gap is larger than four years, for gaps of up to ten years. In addition, we find that image quality and eyewear present more of a challenge than facial hair.},
	keywords = {algorithms;support, databases;, dataset;age, FGnet, hair;gradient, information;image, information;recognition, machine;face, machines;visual, methods;eyewear;face, methods;support, orientation, orientation;gradient, progression;commercial, pyramid;hierarchical, quality;magnitude, recognition;gradient, systems;discriminative, vector, verification;facial},
	isbn = {1556-6013},
	doi = {10.1109/TIFS.2009.2038751},
	author = {Ling,Haibin and Soatto,S. and Ramanathan,N. and Jacobs, David W.}
}
@conference {12482,
	title = {Robust RVM regression using sparse outlier model},
	booktitle = {Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on},
	year = {2010},
	month = {2010/06//},
	pages = {1887 - 1894},
	abstract = {Kernel regression techniques such as Relevance Vector Machine (RVM) regression, Support Vector Regression and Gaussian processes are widely used for solving many computer vision problems such as age, head pose, 3D human pose and lighting estimation. However, the presence of outliers in the training dataset makes the estimates from these regression techniques unreliable. In this paper, we propose robust versions of the RVM regression that can handle outliers in the training dataset. We decompose the noise term in the RVM formulation into a (sparse) outlier noise term and a Gaussian noise term. We then estimate the outlier noise along with the model parameters. We present two approaches for solving this estimation problem: (1) a Bayesian approach, which essentially follows the RVM framework and (2) an optimization approach based on Basis Pursuit Denoising. In the Bayesian approach, the robust RVM problem essentially becomes a bigger RVM problem with the advantage that it can be solved efficiently by a fast algorithm. Empirical evaluations, and real experiments on image de-noising and age estimation demonstrate the better performance of the robust RVM algorithms over that of the RVM reg ression.},
	keywords = {3D, analysis;, approach;Gaussian, denoising;computer, denoising;lighting, denoising;regression, estimation;relevance, human, machine;robust, model;Gaussian, noise;basis, noise;computer, outlier, pose;Bayesian, pursuit, regression;sparse, RVM, vector, vision;image},
	doi = {10.1109/CVPR.2010.5539861},
	author = {Mitra, K. and Veeraraghavan,A. and Chellapa, Rama}
}
@conference {13096,
	title = {Action recognition based on human movement characteristics},
	booktitle = {Motion and Video Computing, 2009. WMVC {\textquoteright}09. Workshop on},
	year = {2009},
	month = {2009/12//},
	pages = {1 - 8},
	abstract = {We present a motion descriptor for human action recognition where appearance and shape information are unreliable. Unlike other motion-based approaches, we leverage image characteristics specific to human movement to achieve better robustness and lower computational cost. Drawing on recent work on motion recognition with ballistic dynamics, an action is modeled as a series of short correlated linear movements and represented with a probability density function over motion vector data. We are targeting common human actions composed of ballistic movements, and our descriptor can handle both short actions (e.g. reaching with the hand) and long actions with events at relatively stable time offsets (e.g. walking). The proposed descriptor is used for both classification and detection of action instances, in a nearest-neighbor framework. We evaluate the descriptor on the KTH action database and obtain a recognition rate of 90\% in a relevant test setting, comparable to the state-of-the-art approaches that use other cues in addition to motion. We also acquired a database of actions with slight occlusion and a human actor manipulating objects of various shapes and appearances. This database makes the use of appearance and shape information problematic, but we obtain a recognition rate of 95\%. Our work demonstrates that human movement has distinctive patterns, and that these patterns can be used effectively for action recognition.},
	keywords = {action, ballistic, characteristics;motion, correlated, cost;human, data;probability, databases;, density, descriptor;motion, dynamics;computational, function;robustness;shape, information;short, linear, Movement, movements;computer, recognition;human, recognition;stability;visual, vector, vision;pattern},
	doi = {10.1109/WMVC.2009.5399233},
	author = {Dondera,R. and David Doermann and Davis, Larry S.}
}
@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 {13144,
	title = {Human Appearance Change Detection},
	booktitle = {Image Analysis and Processing, 2007. ICIAP 2007. 14th International Conference on},
	year = {2007},
	month = {2007/09//},
	pages = {536 - 541},
	abstract = {We present a machine learning approach to detect changes in human appearance between instances of the same person that may be taken with different cameras, but over short periods of time. For each video sequence of the person, we approximately align each frame in the sequence and then generate a set of features that captures the differences between the two sequences. The features are the occupancy difference map, the codeword frequency difference map (based on a vector quantization of the set of colors and frequencies) at each aligned pixel and the histogram intersection map. A boosting technique is then applied to learn the most discriminative set of features, and these features are then used to train a support vector machine classifier to recognize significant appearance changes. We apply our approach to the problem of left package detection. We train the classifiers on a laboratory database of videos in which people are seen with and without common articles that people carry - backpacks and suitcases. We test the approach on some real airport video sequences. Moving to the real world videos requires addressing additional problems, including the view selection problem and the frame selection problem.},
	keywords = {(artificial, appearance, approach;occupancy, change, changes, classification;support, classifier;vector, detection;image, detection;machine, difference, Frequency, intelligence);pattern, intersection, learning, machine, machines;vector, map;histogram, map;human, map;support, package, quantisation;video, quantization;video, recognition;boosting, recognition;image, sequence;left, sequences;image, sequences;learning, surveillance;, technique;codeword, vector},
	doi = {10.1109/ICIAP.2007.4362833},
	author = {Ghanem,N.M. and Davis, Larry S.}
}
@conference {14862,
	title = {A Study of Face Recognition as People Age},
	booktitle = {Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on},
	year = {2007},
	month = {2007/10//},
	pages = {1 - 8},
	abstract = {In this paper we study face recognition across ages within a real passport photo verification task. First, we propose using the gradient orientation pyramid for this task. Discarding the gradient magnitude and utilizing hierarchical techniques, we found that the new descriptor yields a robust and discriminative representation. With the proposed descriptor, we model face verification as a two-class problem and use a support vector machine as a classifier. The approach is applied to two passport data sets containing more than 1,800 image pairs from each person with large age differences. Although simple, our approach outperforms previously tested Bayesian technique and other descriptors, including the intensity difference and gradient with magnitude. In addition, it works as well as two commercial systems. Second, for the first time, we empirically study how age differences affect recognition performance. Our experiments show that, although the aging process adds difficulty to the recognition task, it does not surpass illumination or expression as a confounding factor.},
	keywords = {Bayesian, difference;passport, machine;face, machines;, magnitude;gradient, orientation, photo, pyramid;hierarchical, recognition;face, recognition;support, representation;face, task;support, technique;discriminative, techniques;intensity, vector, verification, verification;gradient},
	doi = {10.1109/ICCV.2007.4409069},
	author = {Ling,Haibin and Soatto,S. and Ramanathan,N. and Jacobs, David W.}
}
@conference {13188,
	title = {Detecting rotational symmetries},
	booktitle = {Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on},
	volume = {2},
	year = {2005},
	month = {2005/10//},
	pages = {954 - 961 Vol. 2 - 954 - 961 Vol. 2},
	abstract = {We present an algorithm for detecting multiple rotational symmetries in natural images. Given an image, its gradient magnitude field is computed, and information from the gradients is spread using a diffusion process in the form of a gradient vector flow (GVF) field. We construct a graph whose nodes correspond to pixels in tire image, connecting points that are likely to be rotated versions of one another The n-cycles present in tire graph are made to vote for C<sub>n</sub> symmetries, their votes being weighted by the errors in transformation between GVF in the neighborhood of the voting points, and the irregularity of the n-sided polygons formed by the voters. The votes are accumulated at tire centroids of possible rotational symmetries, generating a confidence map for each order of symmetry. We tested the method with several natural images.},
	keywords = {axial, computational, detection;, field;, flow;, geometry;, gradient, graph, graph;, image, image;, magnitude, methods;, multiple, n-sided, object, polygons;, recognition;, rotational, symmetries;, symmetry;, theory;, tire, tyres;, vector},
	doi = {10.1109/ICCV.2005.71},
	author = {Shiv Naga Prasad,V. and Davis, Larry S.}
}
@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 {14052,
	title = {Three dimensional acoustic scattering from an M multilayered domain via an integral equation approach},
	booktitle = {Antennas and Propagation Society International Symposium, 2003. IEEE},
	volume = {1},
	year = {2003},
	month = {2003/06//},
	pages = {669 - 672 vol.1 - 669 - 672 vol.1},
	abstract = {An integral equation approach is derived for three dimensional acoustic scattering from an M multilayered domain. The integral equation is solved using the collocation method. To validate the method, we compare the exact and approximate results. The numerous matrix vector multiplications of our method may be thought a disadvantage. This problem can be overcome by using fast multiple methods where these operations are done very quickly.},
	keywords = {acoustic, collocation, dimensional, domain;, equation;, equations;, fast, inhomogeneous, integral, matrix, media;, method;, methods;, multilayered, multilayers;, multiple, multiplication;, multiplications;, scattering;, three, vector, wave},
	doi = {10.1109/APS.2003.1217547},
	author = {Seydou,F. and Duraiswami, Ramani and Seppanen,T.}
}
@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 {13272,
	title = {A Smale-like decomposition for discrete scalar fields},
	booktitle = {Pattern Recognition, 2002. Proceedings. 16th International Conference on},
	volume = {1},
	year = {2002},
	month = {2002///},
	pages = {184 - 187 vol.1 - 184 - 187 vol.1},
	abstract = {In this paper we address the problem of representing the structure of the topology of a d-dimensional scalar field as a basis for constructing a multiresolution representation of the structure of such afield. To this aim, we define a discrete decomposition of a triangulated d-dimensional domain, on whose vertices the values of the field are given. We extend a Smale decomposition, defined by Thom (1949) and Smale (1960) for differentiable functions, to the discrete case, to what we call a Smale-like decomposition. We introduce the notion of discrete gradient vector field, which indicates the growth of the scalar field and matches with our decomposition. We sketch an algorithm for building a Smale-like decomposition and a graph-based representation of this decomposition. We present results for the case of two-dimensional fields.},
	keywords = {data, decomposition;, differentiable, discrete, domain;, field;, fields;, functions;, gradient, graph-based, methods;, multidimensional, multiresolution, representation;, scalar, Smale-like, structure, Topology, triangulated, vector, visualisation;},
	doi = {10.1109/ICPR.2002.1044644},
	author = {De Floriani, Leila and Mesmoudi,M. M. and Danovaro,E.}
}
@conference {15577,
	title = {Algorithms for fast vector quantization},
	booktitle = {Data Compression Conference, 1993. DCC {\textquoteright}93.},
	year = {1993},
	month = {1993///},
	pages = {381 - 390},
	abstract = {This paper shows that if one is willing to relax the requirement of finding the true nearest neighbor, it is possible to achieve significant improvements in running time and at only a very small loss in the performance of the vector quantizer. The authors present three algorithms for nearest neighbor searching: standard and priority <e1>k </e1>-<e1>d</e1> tree search algorithms and a neighborhood graph search algorithm in which a directed graph is constructed for the point set and edges join neighboring points},
	keywords = {algorithm;, algorithms;, directed, fast, graph, graph;, graphs;, Neighborhood, performance;, problems;, quantisation;, quantization;, running, search, time;, tree, vector},
	doi = {10.1109/DCC.1993.253111},
	author = {Arya,S. and Mount, Dave}
}
@article {15049,
	title = {VLSI implementation of a tree searched vector quantizer},
	journal = {Signal Processing, IEEE Transactions on},
	volume = {41},
	year = {1993},
	month = {1993/02//},
	pages = {901 - 905},
	abstract = {The VLSI design and implementation of a tree-searched vector quantizer is presented. The number of processors needed is equal to the depth of the tree. All processors are identical, and data flow between processors is regular. No global control signals are needed. The processors have been fabricated using 2 mu;m <e1>N</e1>-well process on a 7.9 times;9.2 mm die. Each processor chip contains 25000 transistors and has 84 pins. The processors have been thoroughly tested at a clock frequency of 20 MHz},
	keywords = {(mathematics);, 2, 20, chips;, coding;, compression;, data, design;, digital, image, implementation;, MHz;, micron;, PROCESSING, quantisation;, quantizer;, searched, signal, tree, TREES, vector, VLSI, VLSI;},
	isbn = {1053-587X},
	doi = {10.1109/78.193225},
	author = {Kolagotla,R. K. and Yu,S.-S. and JaJa, Joseph F.}
}
