@conference {15029,
	title = {Strategies for exploring large scale data},
	booktitle = {Parallel Architectures, Algorithms and Networks, 2004. Proceedings. 7th International Symposium on},
	year = {2004},
	month = {2004/05//},
	pages = {2 - 2},
	abstract = {We consider the problem of querying large scale multidimensional time series data to discover events of interest, test and validate hypotheses, or to associate temporal patterns with specific events. This type of data currently dominates most other types of available data, and will very likely become even more prevalent in the future given the current trends in collecting time series of business, scientific, demographic, and simulation data. The ability to explore such collections interactively, even at a coarse level, will be critical in discovering the information and knowledge embedded in such collections. We develop indexing techniques and search algorithms to efficiently handle temporal range value querying of multidimensional time series data. Our indexing uses linear space data structures that enable the handling of queries in I/O time that is essentially the same as that of handling a single time slice, assuming the availability of a logarithmic number of processors as a function of the temporal window. A data structure with provably almost optimal asymptotic bounds is also presented for the case when the number of multidimensional objects is relatively small. These techniques improve significantly over standard techniques for either serial or parallel processing, and are evaluated by extensive experimental results that confirm their superior performance.},
	keywords = {algorithms;, association;, asymptotic, bounds;, business, data, data;, database, databases;, demographic, discovery;, Indexing, indexing;, information, knowledge, large, linear, mining;, multidimensional, objects;, optimal, Parallel, pattern, processing;, query, querying;, range, scale, scientific, search, serial, series, series;, simulation, space, structure;, structures;, techniques;, temporal, TIME, value, very, window;},
	doi = {10.1109/ISPAN.2004.1300447},
	author = {JaJa, Joseph F.}
}
@conference {16894,
	title = {Depth-first k-nearest neighbor finding using the MaxNearestDist estimator},
	booktitle = {Image Analysis and Processing, 2003.Proceedings. 12th International Conference on},
	year = {2003},
	month = {2003/09//},
	pages = {486 - 491},
	abstract = {Similarity searching is an important task when trying to find patterns in applications which involve mining different types of data such as images, video, time series, text documents, DNA sequences, etc. Similarity searching often reduces to finding the k nearest neighbors to a query object. A description is given of how to use an estimate of the maximum possible distance at which a nearest neighbor can be found to prune the search process in a depth-first branch-and-bound k-nearest neighbor finding algorithm. Using the MaxNearestDist estimator (Larsen, S. and Kanal, L.N., 1986) in the depth-first k-nearest neighbor algorithm provides a middle ground between a pure depth-first and a best-first k-nearest neighbor algorithm.},
	keywords = {branch-and-bound, data, depth-first, distance;, DNA, documents;, estimation;, estimator;, finding;, images;, k-nearest, matching;, maximum, MaxNearestDist, mining;, neighbor, parameter, pattern, possible, process;, processing;, query, search, searching;, sequences;, series;, similarity, text, TIME, tree, video;},
	doi = {10.1109/ICIAP.2003.1234097},
	author = {Samet, Hanan}
}
@conference {12732,
	title = {Simultaneous tracking and recognition of human faces from video},
	booktitle = {Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP {\textquoteright}03). 2003 IEEE International Conference on},
	volume = {3},
	year = {2003},
	month = {2003/04//},
	pages = {III - 225-8 vol.3 - III - 225-8 vol.3},
	abstract = {The paper investigates the interaction between tracking and recognition of human faces from video under a framework proposed earlier (Shaohua Zhou et al., Proc. 5th Int. Conf. on Face and Gesture Recog., 2002; Shaohua Zhou and Chellappa, R., Proc. European Conf. on Computer Vision, 2002), where a time series model is used to resolve the uncertainties in both tracking and recognition. However, our earlier efforts employed only a simple likelihood measurement in the form of a Laplacian density to deal with appearance changes between frames and between the observation and gallery images, yielding poor accuracies in both tracking and recognition when confronted by pose and illumination variations. The interaction between tracking and recognition was not well understood. We address the interdependence between tracking and recognition using a series of experiments and quantify the interacting nature of tracking and recognition.},
	keywords = {appearance, changes;, density;, Face, human, illumination, Laplacian, model;, optical, pose, processing;, recognition;, series, series;, signal, TIME, tracking;, variations;, video, video;},
	doi = {10.1109/ICASSP.2003.1199148},
	author = {Zhou,Shaohua and Chellapa, Rama}
}
@conference {12756,
	title = {A robust algorithm for probabilistic human recognition from video},
	booktitle = {Pattern Recognition, 2002. Proceedings. 16th International Conference on},
	volume = {1},
	year = {2002},
	month = {2002///},
	pages = {226 - 229 vol.1 - 226 - 229 vol.1},
	abstract = {Human recognition from video requires solving the two tasks, recognition and tracking, simultaneously. This leads to a parameterized time series state space model, representing both motion and identity of the human. Sequential Monte Carlo (SMC) algorithms, like Condensation, can be developed to offer numerical solutions to this model. However in outdoor environments, the solution is more likely to diverge from the foreground, causing failures in both recognition and tracking. In this paper we propose an approach for tackling this problem by incorporating the constraint of temporal continuity in the observations. Experimental results demonstrate improvements over its Condensation counterpart.},
	keywords = {algorithm;, algorithms;, Carlo, continuity;, human, image, methods;, model;, Monte, parameterized, probabilistic, recognition;, robust, sequential, series, series;, space, state, state-space, temporal, TIME},
	doi = {10.1109/ICPR.2002.1044661},
	author = {Zhou,Shaohua and Chellapa, Rama}
}
