Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Name that tune: a pilot study in finding a melody from a sung query
Journal of the American Society for Information Science and Technology
Robust and fast similarity search for moving object trajectories
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Hidden Conditional Random Fields for Gesture Recognition
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Exact indexing of dynamic time warping
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
On the marriage of Lp-norms and edit distance
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Challenging Uncertainty in Query by Humming Systems: A Fingerprinting Approach
IEEE Transactions on Audio, Speech, and Language Processing
Classification of musical patterns using variable duration hidden Markov models
IEEE Transactions on Audio, Speech, and Language Processing
Fast variable selection for memetracker phrases time series prediction
Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments
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An important theoretical topic in assistive environments is reasoning about temporal patterns, that represent the sequential output of various sensors, and that can give us information about the health and activities of humans and the state of the environment. The recent growth in the quantity and quality of sensors for assistive environments has made it possible to create large databases of temporal patterns, that store sequences of observations obtained from such sensors over large time intervals. A topic of significant interest is being able to search such large databases so as to identify content of interest, for example activities of a certain type, or information about a patient's well-being. In this paper, we study two different approaches for conducting such searches: an exemplar-based approach, where we describe what we are looking for by giving an example, and a model-based approach, where we describe what we are looking for via a generative model. In particular, we describe the two different approaches, and we identify some important pros and cons for each approach. We also perform a comparative evaluation of exemplar-based search using dynamic time warping (DTW), and model-based search using Hidden Markov Models (HMMs), on large real datasets. In our experiments, when the number of training objects per model is sufficiently high, model-based search using HMMs produces more accurate search results than exemplar-based search using DTW.