@conference {12721,
	title = {An appearance based approach for human and object tracking},
	booktitle = {Image Processing, 2003. ICIP 2003. Proceedings. 2003 International Conference on},
	volume = {2},
	year = {2003},
	month = {2003/09//},
	pages = {II - 85-8 vol.3 - II - 85-8 vol.3},
	abstract = {A system for tracking humans and detecting human-object interactions in indoor environments is described. A combination of correlogram and histogram information is used to model object and human color distributions. Humans and objects are detected using a background subtraction algorithm. The models are built on the fly and used to track them on a frame by frame basis. The system is able to detect when people merge into groups and segment them during occlusion. Identities are preserved during the sequence, even if a person enters and leaves the scene. The system is also able to detect when a person deposits or removes an object from the scene. In the first case the models are used to track the object retroactively in time. In the second case the objects are tracked for the rest of the sequence. Experimental results using indoor video sequences are presented.},
	keywords = {algorithm;, analysis;, background, basis;, by, Color, colour, correlogram, detection;, distributions;, frame, histogram, human, image, information;, object, processing;, segmentation;, sequences;, signal, subtraction, tracking;, video},
	doi = {10.1109/ICIP.2003.1246622},
	author = {Capellades,M. B and David Doermann and DeMenthon,D. and Chellapa, Rama}
}
@conference {13247,
	title = {Learning dynamics for exemplar-based gesture recognition},
	booktitle = {Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on},
	volume = {1},
	year = {2003},
	month = {2003/06//},
	pages = {I-571 - I-578 vol.1 - I-571 - I-578 vol.1},
	abstract = {This paper addresses the problem of capturing the dynamics for exemplar-based recognition systems. Traditional HMM provides a probabilistic tool to capture system dynamics and in exemplar paradigm, HMM states are typically coupled with the exemplars. Alternatively, we propose a non-parametric HMM approach that uses a discrete HMM with arbitrary states (decoupled from exemplars) to capture the dynamics over a large exemplar space where a nonparametric estimation approach is used to model the exemplar distribution. This reduces the need for lengthy and non-optimal training of the HMM observation model. We used the proposed approach for view-based recognition of gestures. The approach is based on representing each gesture as a sequence of learned body poses (exemplars). The gestures are recognized through a probabilistic framework for matching these body poses and for imposing temporal constraints between different poses using the proposed non-parametric HMM.},
	keywords = {arbitrary, body, by, Computer, constraint;, detection;, discrete, distribution, dynamics;, edge, estimation;, example;, exemplar, exemplar-based, extraction;, feature, framework;, gesture, gesture;, hidden, HMM;, human, image, learning, Markov, matching;, model;, models;, motion;, nonparametric, pose, probabilistic, recognition;, sequence;, space;, state;, statistics;, system, temporal, tool;, view-based, vision;},
	doi = {10.1109/CVPR.2003.1211405},
	author = {Elgammal,A. and Shet,V. and Yacoob,Yaser and Davis, Larry S.}
}
@article {14891,
	title = {Classification with nonmetric distances: image retrieval and class representation},
	journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
	volume = {22},
	year = {2000},
	month = {2000/06//},
	pages = {583 - 600},
	abstract = {A key problem in appearance-based vision is understanding how to use a set of labeled images to classify new images. Systems that model human performance, or that use robust image matching methods, often use nonmetric similarity judgments; but when the triangle inequality is not obeyed, most pattern recognition techniques are not applicable. Exemplar-based (nearest-neighbor) methods can be applied to a wide class of nonmetric similarity functions. The key issue, however, is to find methods for choosing good representatives of a class that accurately characterize it. We show that existing condensing techniques are ill-suited to deal with nonmetric dataspaces. We develop techniques for solving this problem, emphasizing two points: First, we show that the distance between images is not a good measure of how well one image can represent another in nonmetric spaces. Instead, we use the vector correlation between the distances from each image to other previously seen images. Second, we show that in nonmetric spaces, boundary points are less significant for capturing the structure of a class than in Euclidean spaces. We suggest that atypical points may be more important in describing classes. We demonstrate the importance of these ideas to learning that generalizes from experience by improving performance. We also suggest ways of applying parametric techniques to supervised learning problems that involve a specific nonmetric distance functions, showing how to generalize the idea of linear discriminant functions in a way that may be more useful in nonmetric spaces},
	keywords = {appearance-based, by, classification;image, correlation;correlation, dataspaces;nonmetric, distances;nonmetric, example;, functions;nonmetric, image, inequality;vector, judgments;robust, MATCHING, methods;image, methods;nonmetric, methods;triangle, points;boundary, points;class, representation;exemplar-based, representation;image, retrieval;learning, retrieval;nearest-neighbor, similarity, vision;atypical},
	isbn = {0162-8828},
	doi = {10.1109/34.862197},
	author = {Jacobs, David W. and Weinshall,D. and Gdalyahu,Y.}
}
