@article {13066,
	title = {Face Identification Using Large Feature Sets},
	journal = {Image Processing, IEEE Transactions on},
	volume = {21},
	year = {2012},
	month = {2012/04//},
	pages = {2245 - 2255},
	abstract = {With the goal of matching unknown faces against a gallery of known people, the face identification task has been studied for several decades. There are very accurate techniques to perform face identification in controlled environments, particularly when large numbers of samples are available for each face. However, face identification under uncontrolled environments or with a lack of training data is still an unsolved problem. We employ a large and rich set of feature descriptors (with more than 70 000 descriptors) for face identification using partial least squares to perform multichannel feature weighting. Then, we extend the method to a tree-based discriminative structure to reduce the time required to evaluate probe samples. The method is evaluated on Facial Recognition Technology (FERET) and Face Recognition Grand Challenge (FRGC) data sets. Experiments show that our identification method outperforms current state-of-the-art results, particularly for identifying faces acquired across varying conditions.},
	keywords = {(mathematics);, approximations;trees, challenge, data, data;tree-based, discriminative, environments;face, feature, FERET;FRGC, grand, identification, least, recognition, recognition;least, sets;face, sets;facial, squares, squares;training, structure;uncontrolled, task;face, technology;multichannel, weighting;partial},
	isbn = {1057-7149},
	doi = {10.1109/TIP.2011.2176951},
	author = {Schwartz, W.R. and Guo,Huimin and Choi,Jonghyun 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.}
}
@conference {14834,
	title = {Bypassing synthesis: PLS for face recognition with pose, low-resolution and sketch},
	booktitle = {Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on},
	year = {2011},
	month = {2011/06//},
	pages = {593 - 600},
	abstract = {This paper presents a novel way to perform multi-modal face recognition. We use Partial Least Squares (PLS) to linearly map images in different modalities to a common linear subspace in which they are highly correlated. PLS has been previously used effectively for feature selection in face recognition. We show both theoretically and experimentally that PLS can be used effectively across modalities. We also formulate a generic intermediate subspace comparison framework for multi-modal recognition. Surprisingly, we achieve high performance using only pixel intensities as features. We experimentally demonstrate the highest published recognition rates on the pose variations in the PIE data set, and also show that PLS can be used to compare sketches to photos, and to compare images taken at different resolutions.},
	keywords = {approximations;pose, COMPARISON, estimation;, extraction;image, framework;face, intensities;subspace, least, PLS;bypassing, recognition;feature, recognition;partial, resolution;least, selection;multimodal, squares, squares;pixel, synthesis;face},
	doi = {10.1109/CVPR.2011.5995350},
	author = {Sharma,A. and Jacobs, David W.}
}
@conference {13068,
	title = {Face verification using large feature sets and one shot similarity},
	booktitle = {Biometrics (IJCB), 2011 International Joint Conference on},
	year = {2011},
	month = {2011/10//},
	pages = {1 - 8},
	abstract = {We present a method for face verification that combines Partial Least Squares (PLS) and the One-Shot similarity model[28]. First, a large feature set combining shape, texture and color information is used to describe a face. Then PLS is applied to reduce the dimensionality of the feature set with multi-channel feature weighting. This provides a discriminative facial descriptor. PLS regression is used to compute the similarity score of an image pair by One-Shot learning. Given two feature vector representing face images, the One-Shot algorithm learns discriminative models exclusively for the vectors being compared. A small set of unlabeled images, not containing images belonging to the people being compared, is used as a reference (negative) set. The approach is evaluated on the Labeled Face in the Wild (LFW) benchmark and shows very comparable results to the state-of-the-art methods (achieving 86.12\% classification accuracy) while maintaining simplicity and good generalization ability.},
	keywords = {analysis;set, approximations;regression, descriptor;labeled, Face, feature, in, information;face, information;texture, least, LFW;PLS;PLS, recognition;least, regression;color, sets;one, shot, similarity;partial, squares, squares;shape, the, theory;, verification;facial, wild;large},
	doi = {10.1109/IJCB.2011.6117498},
	author = {Guo,Huimin and Robson Schwartz,W. and Davis, Larry S.}
}
@conference {13943,
	title = {A partial least squares framework for speaker recognition},
	booktitle = {Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on},
	year = {2011},
	month = {2011/05//},
	pages = {5276 - 5279},
	abstract = {Modern approaches to speaker recognition (verification) operate in a space of "supervectors" created via concatenation of the mean vectors of a Gaussian mixture model (GMM) adapted from a universal background model (UBM). In this space, a number of approaches to model inter-class separability and nuisance attribute variability have been proposed. We develop a method for modeling the variability associated with each class (speaker) by using partial-least-squares - a latent variable modeling technique, which isolates the most informative subspace for each speaker. The method is tested on NIST SRE 2008 data and provides promising results. The method is shown to be noise-robust and to be able to efficiently learn the subspace corresponding to a speaker on training data consisting of multiple utterances.},
	keywords = {approximations;speaker, attribute, background, GMM;Gaussian, least, mixture, model;Gaussian, model;NIST, modeling, processes;least, recognition;, recognition;speaker, separability;latent, squares, squares;partial-least-squares;speaker, SRE;interclass, technique;multiple, utterances;nuisance, variability;partial, variable, verification;universal},
	doi = {10.1109/ICASSP.2011.5947548},
	author = {Srinivasan,B.V. and Zotkin,Dmitry N and Duraiswami, Ramani}
}
@conference {13102,
	title = {Learning Discriminative Appearance-Based Models Using Partial Least Squares},
	booktitle = {Computer Graphics and Image Processing (SIBGRAPI), 2009 XXII Brazilian Symposium on},
	year = {2009},
	month = {2009/10//},
	pages = {322 - 329},
	abstract = {Appearance information is essential for applications such as tracking and people recognition. One of the main problems of using appearance-based discriminative models is the ambiguities among classes when the number of persons being considered increases. To reduce the amount of ambiguity, we propose the use of a rich set of feature descriptors based on color, textures and edges. Another issue regarding appearance modeling is the limited number of training samples available for each appearance. The discriminative models are created using a powerful statistical tool called partial least squares (PLS), responsible for weighting the features according to their discriminative power for each different appearance. The experimental results, based on appearance-based person recognition, demonstrate that the use of an enriched feature set analyzed by PLS reduces the ambiguity among different appearances and provides higher recognition rates when compared to other machine learning techniques.},
	keywords = {(artificial, analysis;learning, appearance, approximations;object, based, colour, descriptors;learning, discriminative, intelligence);least, learning, least, models;machine, person, recognition;, recognition;feature, squares, squares;image, techniques;partial},
	doi = {10.1109/SIBGRAPI.2009.42},
	author = {Schwartz, W.R. and Davis, Larry S.}
}
@conference {14863,
	title = {Sparse representation of cast shadows via l1-regularized least squares},
	booktitle = {Computer Vision, 2009 IEEE 12th International Conference on},
	year = {2009},
	month = {2009///},
	pages = {583 - 590},
	abstract = {Scenes with cast shadows can produce complex sets of images. These images cannot be well approximated by low-dimensional linear subspaces. However, in this paper we show that the set of images produced by a Lambertian scene with cast shadows can be efficiently represented by a sparse set of images generated by directional light sources. We first model an image with cast shadows as composed of a diffusive part (without cast shadows) and a residual part that captures cast shadows. Then, we express the problem in an $\#$x2113;<sub>1</sub>-regularized least squares formulation, with nonnegativity constraints. This sparse representation enjoys an effective and fast solution, thanks to recent advances in compressive sensing. In experiments on both synthetic and real data, our approach performs favorably in comparison to several previously proposed methods.},
	keywords = {$\#$x2113;<sub>1</sub>-regularized, approximations;lighting;, formulation;Lambertian, least, representation;image, representation;least, scene;cast, sensing;sparse, shadows;compressive, squares},
	doi = {10.1109/ICCV.2009.5459185},
	author = {Mei,Xue and Ling,Haibin and Jacobs, David W.}
}
@conference {12581,
	title = {Kernel fully constrained least squares abundance estimates},
	booktitle = {Geoscience and Remote Sensing Symposium, 2007. IGARSS 2007. IEEE International},
	year = {2007},
	month = {2007/07//},
	pages = {4041 - 4044},
	abstract = {A critical step for fitting a linear mixing model to hyperspectral imagery is the estimation of the abundances. The abundances are the percentage of each end member within a given pixel; therefore, they should be non-negative and sum to one. With the advent of kernel based algorithms for hyperspectral imagery, kernel based abundance estimates have become necessary. This paper presents such an algorithm that estimates the abundances in the kernel feature space while maintaining the non-negativity and sum-to-one constraints. The usefulness of the algorithm is shown using the AVIRIS Cuprite, Nevada image.},
	keywords = {abundance, algorithm;kernel, analysis;, AVIRIS, based, constrained, constraint;feature, constraint;spectral, estimates;linear, extraction;geophysical, feature, fully, image;hyperspectral, imagery;kernel, least, mixing, model;nonnegativity, processing;geophysical, processing;multidimensional, processing;spectral, signal, space;kernel, squares, techniques;image, unmixing;sum-to-one},
	doi = {10.1109/IGARSS.2007.4423736},
	author = {Broadwater, J. and Chellapa, Rama and Banerjee, A. and Burlina, P.}
}
@conference {13145,
	title = {Multimodal Tracking for Smart Videoconferencing and Video Surveillance},
	booktitle = {Computer Vision and Pattern Recognition, 2007. CVPR {\textquoteright}07. IEEE Conference on},
	year = {2007},
	month = {2007/06//},
	pages = {1 - 2},
	abstract = {Many applications require the ability to track the 3-D motion of the subjects. We build a particle filter based framework for multimodal tracking using multiple cameras and multiple microphone arrays. In order to calibrate the resulting system, we propose a method to determine the locations of all microphones using at least five loudspeakers and under assumption that for each loudspeaker there exists a microphone very close to it. We derive the maximum likelihood (ML) estimator, which reduces to the solution of the non-linear least squares problem. We verify the correctness and robustness of the multimodal tracker and of the self-calibration algorithm both with Monte-Carlo simulations and on real data from three experimental setups.},
	keywords = {(numerical, 3D, algorithm;smart, analysis;least, approximations;particle, arrays;nonlinear, cameras;multiple, Carlo, estimator;multimodal, filter;self-calibration, Filtering, least, likelihood, methods);teleconferencing;video, methods;image, microphone, MOTION, motion;Monte-Carlo, problem;particle, processing;video, signal, simulations;maximum, squares, surveillance;, surveillance;Monte, tracking;multiple, videoconferencing;video},
	doi = {10.1109/CVPR.2007.383525},
	author = {Zotkin,Dmitry N and Raykar,V.C. and Duraiswami, Ramani and Davis, Larry S.}
}
@conference {12665,
	title = {Moving Object Segmentation and Dynamic Scene Reconstruction Using Two Frames},
	booktitle = {Acoustics, Speech, and Signal Processing, 2005. Proceedings. (ICASSP {\textquoteright}05). IEEE International Conference on},
	volume = {2},
	year = {2005},
	month = {2005//18/23},
	pages = {705 - 708},
	keywords = {3D, analysis;, constraints;, dynamic, ego-motion, estimation;, flow, image, images;, independent, INTENSITY, least, mean, median, method;, methods;, model;, MOTION, motion;, moving, object, of, parallax, parallax;, parametric, processing;, reconstruction;, scene, segmentation;, signal, squares, squares;, static, structure;, subspace, surface, translational, two-frame, unconstrained, video},
	doi = {10.1109/ICASSP.2005.1415502},
	author = {Agrawala, Ashok K. and Chellapa, Rama}
}
@conference {14035,
	title = {Automatic position calibration of multiple microphones},
	booktitle = {Acoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP {\textquoteright}04). IEEE International Conference on},
	volume = {4},
	year = {2004},
	month = {2004/05//},
	pages = {iv-69 - iv-72 vol.4 - iv-69 - iv-72 vol.4},
	abstract = {We describe a method to determine automatically the relative three dimensional positions of multiple microphones using at least five loudspeakers in unknown positions. The only assumption we make is that there is a microphone which is very close to a loudspeaker. In our experimental setup, we attach one microphone to each loudspeaker. We derive the maximum likelihood estimator and the solution turns out to be a non-linear least squares problem. A closed form solution which can be used as the initial guess for the minimization routine is derived. We also derive an approximate expression for the covariance of the estimator using the implicit function theorem. Using this, we analyze the performance of the estimator with respect to the positions of the loudspeakers. The algorithm is validated using both Monte-Carlo simulations and a real-time experimental setup.},
	keywords = {approximations;, array, audio, AUTOMATIC, calibration;, closed, covariance;, dimensional, estimation;, form, function, implicit, least, likelihood, loudspeakers;, maximum, microphone, microphones;, minimisation;, minimization;, multiple, nonlinear, position, positions;, problem;, processing;, signal, solution;, squares, theorem;, three},
	doi = {10.1109/ICASSP.2004.1326765},
	author = {Raykar,V.C. and Duraiswami, Ramani}
}
@conference {12691,
	title = {A hybrid algorithm for subpixel detection in hyperspectral imagery},
	booktitle = {Geoscience and Remote Sensing Symposium, 2004. IGARSS {\textquoteright}04. Proceedings. 2004 IEEE International},
	volume = {3},
	year = {2004},
	month = {2004/09//},
	pages = {1601 -1604 vol.3 - 1601 -1604 vol.3},
	abstract = {Numerous subpixel detection algorithms utilizing structured backgrounds have been developed over the past few years. These range from detection schemes based on spectral unmixing to generalized likelihood ratio tests. Spectral unmixing algorithms such as the Fully Constrained Least Squares (FCLS) algorithm have the advantage of physically modeling the interactions of spectral signatures based on reflectance/emittance spectroscopy. Generalized likelihood ratio tests like the Adaptive Matched Subspace Detector (AMSD) have the advantage of identifying targets that are statistically different from the background. Therefore, a hybrid detector based on both AMSD and FCLS was developed to take advantage of each detector{\textquoteright}s strengths. Results demonstrate that the hybrid detector achieved the lowest false alarm rates while also producing meaningful abundance estimates},
	keywords = {alarm, algorithm;abundance, algorithm;Fully, algorithm;hybrid, algorithm;statistical, AMSD;Adaptive, analysis;structured, approximations;maximum, backgrounds;subpixel, constrained, detection;alarm, detection;target, Detector;FCLS, detector;hyperspectral, estimation;, estimation;emittance, identification;adaptive, imagery;reflectance, least, likelihood, Matched, matching;least, processing;geophysical, rate;generalized, ratio, signal, signatures;spectral, spectra;false, spectra;spectral, squares, subspace, systems;geophysical, techniques;image, tests;hybrid, unmixing},
	doi = {10.1109/IGARSS.2004.1370633},
	author = {Broadwater, J. and Meth, R. and Chellapa, Rama}
}
@conference {14855,
	title = {Stochastic completion fields: a neural model of illusory contour shape and salience},
	booktitle = {Computer Vision, 1995. Proceedings., Fifth International Conference on},
	year = {1995},
	month = {1995/06//},
	pages = {408 - 415},
	abstract = {We describe an algorithm and representation level theory of illusory contour shape and salience. Unlike previous theories, our model is derived from a single assumption-namely, that the prior probability distribution of boundary completion shape can be modeled by a random walk in a lattice whose points are positions and orientations in the image plane (i.e. the space which one can reasonably assume is represented by neurons of the mammalian visual cortex). Our model does not employ numerical relaxation or other explicit minimization, but instead relies on the fact that the probability that a particle following a random walk will pass through a given position and orientation on a path joining two boundary fragments can be computed directly as the product of two vector-field convolutions. We show that for the random walk we define, the maximum likelihood paths are curves of least energy, that is, on average, random walks follow paths commonly assumed to model the shape of illusory contours. A computer model is demonstrated on numerous illusory contour stimuli from the literature},
	keywords = {boundary, completion, computational, Computer, contour, contours;, convolutions;, cortex;, curves, detection;, distribution;, edge, energy;, estimation;, fields;, fragments;, geometry;, illusory, image, lattice;, least, likelihood, mammalian, maximum, model;, nets;, neural, of, paths;, plane;, probability, probability;, random, recognition;, shape;, stimuli;, Stochastic, vector-field, visual, walk;},
	doi = {10.1109/ICCV.1995.466910},
	author = {Williams,L. R and Jacobs, David W.}
}
