@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 {13123,
	title = {Context and observation driven latent variable model for human pose estimation},
	booktitle = {Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on},
	year = {2008},
	month = {2008/06//},
	pages = {1 - 8},
	abstract = {Current approaches to pose estimation and tracking can be classified into two categories: generative and discriminative. While generative approaches can accurately determine human pose from image observations, they are computationally expensive due to search in the high dimensional human pose space. On the other hand, discriminative approaches do not generalize well, but are computationally efficient. We present a hybrid model that combines the strengths of the two in an integrated learning and inference framework. We extend the Gaussian process latent variable model (GPLVM) to include an embedding from observation space (the space of image features) to the latent space. GPLVM is a generative model, but the inclusion of this mapping provides a discriminative component, making the model observation driven. Observation Driven GPLVM (OD-GPLVM) not only provides a faster inference approach, but also more accurate estimates (compared to GPLVM) in cases where dynamics are not sufficient for the initialization of search in the latent space. We also extend OD-GPLVM to learn and estimate poses from parameterized actions/gestures. Parameterized gestures are actions which exhibit large systematic variation in joint angle space for different instances due to difference in contextual variables. For example, the joint angles in a forehand tennis shot are function of the height of the ball (Figure 2). We learn these systematic variations as a function of the contextual variables. We then present an approach to use information from scene/objects to provide context for human pose estimation for such parameterized actions.},
	keywords = {estimation;, estimation;image, Gaussian, gestures;pose, latent, learning;parameterized, model;human, observations;integrated, pose, process, processes;gesture, processing;pose, recognition;image, tracking;Gaussian, variable},
	doi = {10.1109/CVPR.2008.4587511},
	author = {Gupta,A. and Chen,T. and Chen,F. and Kimber,D. and Davis, Larry S.}
}
@article {18193,
	title = {Data hiding in image and video .I. Fundamental issues and solutions},
	journal = {Image Processing, IEEE Transactions on},
	volume = {12},
	year = {2003},
	month = {2003/06//},
	pages = {685 - 695},
	abstract = {We address a number of fundamental issues of data hiding in image and video and propose general solutions to them. We begin with a review of two major types of embedding, based on which we propose a new multilevel embedding framework to allow the amount of extractable data to be adaptive according to the actual noise condition. We then study the issues of hiding multiple bits through a comparison of various modulation and multiplexing techniques. Finally, the nonstationary nature of visual signals leads to highly uneven distribution of embedding capacity and causes difficulty in data hiding. We propose an adaptive solution switching between using constant embedding rate with shuffling and using variable embedding rate with embedded control bits. We verify the effectiveness of our proposed solutions through analysis and simulation.},
	keywords = {adaptive, analysis;, bits;, colour, condition;, constant, CONTROL, data, embedded, EMBEDDING, embedding;, encapsulation;, extractable, hiding;, image, Modulation, modulation;, multilevel, multiplexing, multiplexing;, NOISE, nonstationary, processing;, rate;, reviews;, shuffling;, signal, signals;, simulation;, solution;, techniques;, variable, video, visual},
	isbn = {1057-7149},
	doi = {10.1109/TIP.2003.810588},
	author = {M. Wu and Liu,Bede}
}
