Improving Activity Discovery with Automatic Neighborhood Estimation
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 2814-2818.
Abstract
A fundamental problem for artificial intelligence is identifying perceptual primitives from raw sensory signals that are useful for higher-level reasoning. We equate these primitives with initially unknown recurring patterns called motifs. Autonomously learning the motifs is difficult because their number, location, length, and shape are all unknown. Furthermore, nonlinear temporal warping may be required to ensure the similarity of motif occurrences. In this paper, we extend a leading motif discovery algorithm by allowing it to operate on multidimensional sensor data, incorporating automatic parameter estimation, and providing for motif-specific similarity adaptation. We evaluate our algorithm on several data sets and show how our approach leads to faster real world discovery and more accurate motifs compared to other leading methods.
@inproceedings{ijcai2007p452,
title = {Improving Activity Discovery with Automatic Neighborhood Estimation},
author = {Minnen, David and Starner, Thad and Essa, Irfan A. and Jr., Charles Lee Isbell},
booktitle = {Proceedings of the Twentieth International Joint Conference on
Artificial Intelligence, {IJCAI-07}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ramon López de Mántaras},
pages = {2814--2818},
year = {2007},
url = {https://ijcai.org/proceedings/2007/452},
}
TY - CPAPER TI - Improving Activity Discovery with Automatic Neighborhood Estimation AU - Minnen, David AU - Starner, Thad AU - Essa, Irfan A. AU - Jr., Charles Lee Isbell T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 2814 EP - 2818 UR - https://ijcai.org/proceedings/2007/452 ER -Copied
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