A searchable list of some of my publications is below. You can also access my publications from the following sites.
My ORCID is
https://orcid.org/0000-0002-6236-2969
Publications:
1.
R. Hamid, S. Maddi, A. Bobick, I. Essa
Structure from Statistics - Unsupervised Activity Analysis using Suffix Trees Proceedings Article
In: IEEE International Conference on Computer Vision (ICCV), IEEE Computer Society Press, 2007.
Abstract | Links | BibTeX | Tags: activity discovery, activity recognition, computer vision, ICCV, IEEE
@inproceedings{2007-Hamid-SFSUAAUST,
title = {Structure from Statistics - Unsupervised Activity Analysis using Suffix Trees},
author = {R. Hamid and S. Maddi and A. Bobick and I. Essa},
url = {http://dx.doi.org/10.1109/ICCV.2007.4408894
},
doi = {10.1109/ICCV.2007.4408894},
year = {2007},
date = {2007-10-14},
urldate = {2007-10-14},
booktitle = {IEEE International Conference on Computer Vision (ICCV)},
publisher = {IEEE Computer Society Press},
abstract = {Models of activity structure for unconstrained environments are generally not available a priori. Recent representational approaches to this end are limited by their computational complexity and ability to capture activity structure only up to some fixed temporal scale. In this work, we propose Suffix Trees as an activity representation to efficiently extract the structure of activities by analyzing their constituent event subsequences over multiple temporal scales. We empirically compare Suffix Trees with some of the previous approaches in terms of feature cardinality, discriminative prowess, noise sensitivity, and activity-class discovery. Finally, exploiting the properties of Suffix Trees, we present a novel perspective on anomalous subsequences of activities and propose an algorithm to detect them in linear time. We present comparative results over experimental data collected from a kitchen environment to demonstrate the competence of our proposed framework.
},
keywords = {activity discovery, activity recognition, computer vision, ICCV, IEEE},
pubstate = {published},
tppubtype = {inproceedings}
}
Models of activity structure for unconstrained environments are generally not available a priori. Recent representational approaches to this end are limited by their computational complexity and ability to capture activity structure only up to some fixed temporal scale. In this work, we propose Suffix Trees as an activity representation to efficiently extract the structure of activities by analyzing their constituent event subsequences over multiple temporal scales. We empirically compare Suffix Trees with some of the previous approaches in terms of feature cardinality, discriminative prowess, noise sensitivity, and activity-class discovery. Finally, exploiting the properties of Suffix Trees, we present a novel perspective on anomalous subsequences of activities and propose an algorithm to detect them in linear time. We present comparative results over experimental data collected from a kitchen environment to demonstrate the competence of our proposed framework.
2.
D. Minnen, C. Isbell, I. Essa, T. Starner
Discovering Multivariate Motifs using Subsequence DensityEstimation Proceedings Article
In: American Association of Artificial Intelligence Conference (AAAI), AAAI 2007.
Abstract | Links | BibTeX | Tags: activity discovery, motif discovery, unsupervised learning
@inproceedings{2007-Minnen-DMMUSD,
title = {Discovering Multivariate Motifs using Subsequence DensityEstimation},
author = {D. Minnen and C. Isbell and I. Essa and T. Starner},
url = {http://www.aaai.org/Library/AAAI/2007/aaai07-097.php},
year = {2007},
date = {2007-04-01},
booktitle = {American Association of Artificial Intelligence Conference (AAAI)},
organization = {AAAI},
abstract = {The problem of locating motifs in real-valued, multivariate time series data involves the discovery of sets of recurring patterns embedded in the time series. Each set is composed of several non-overlapping subsequences and constitutes a motif because all of the included subsequences are similar. The ability to automatically discover such motifs allows intelligent systems to form endogenously meaningful representations of their environment through unsupervised sensor analysis. In this paper, we formulate a unifying view of motif discovery as a problem of locating regions of high density in the space of all time series subsequences. Our approach is efficient (sub-quadratic in the length of the data), requires fewer user-specified parameters than previous methods, and naturally allows variable length motif occurrences and non-linear temporal warping. We evaluate the performance of our approach using four data sets from different domains including on-body inertial sensors and speech.},
keywords = {activity discovery, motif discovery, unsupervised learning},
pubstate = {published},
tppubtype = {inproceedings}
}
The problem of locating motifs in real-valued, multivariate time series data involves the discovery of sets of recurring patterns embedded in the time series. Each set is composed of several non-overlapping subsequences and constitutes a motif because all of the included subsequences are similar. The ability to automatically discover such motifs allows intelligent systems to form endogenously meaningful representations of their environment through unsupervised sensor analysis. In this paper, we formulate a unifying view of motif discovery as a problem of locating regions of high density in the space of all time series subsequences. Our approach is efficient (sub-quadratic in the length of the data), requires fewer user-specified parameters than previous methods, and naturally allows variable length motif occurrences and non-linear temporal warping. We evaluate the performance of our approach using four data sets from different domains including on-body inertial sensors and speech.
3.
Raffay Hamid, Siddhartha Maddi, Amos Johnson, Aaron Bobick, Irfan Essa, Charles Isbell
Unsupervised Activity Discovery and Characterization From Event-Streams Proceedings Article
In: Proceedings of The Learning Workshop at Snowbird, Snowbird, Utah, 2005.
Abstract | Links | BibTeX | Tags: activity discovery, activity recognition, computer vision, machine learning
@inproceedings{2005-Hamid-UADCFE,
title = {Unsupervised Activity Discovery and Characterization From Event-Streams},
author = {Raffay Hamid and Siddhartha Maddi and Amos Johnson and Aaron Bobick and Irfan Essa and Charles Isbell},
url = {https://arxiv.org/abs/1207.1381
https://arxiv.org/pdf/1207.1381},
doi = {10.48550/arXiv.1207.1381},
year = {2005},
date = {2005-01-01},
urldate = {2005-01-01},
booktitle = {Proceedings of The Learning Workshop at Snowbird},
address = {Snowbird, Utah},
abstract = {We present a framework to discover and characterize different classes of everyday activities from event-streams. We begin by representing activities as bags of event n-grams. This allows us to analyze the global structural information of activities, using their local event statistics. We demonstrate how maximal cliques in an undirected edge-weighted graph of activities, can be used for activity-class discovery in an unsupervised manner. We show how modeling an activity as a variable length Markov process, can be used to discover recurrent event-motifs to characterize the discovered activity-classes. We present results over extensive data-sets, collected from multiple active environments, to show the competence and generalizability of our proposed framework.
},
keywords = {activity discovery, activity recognition, computer vision, machine learning},
pubstate = {published},
tppubtype = {inproceedings}
}
We present a framework to discover and characterize different classes of everyday activities from event-streams. We begin by representing activities as bags of event n-grams. This allows us to analyze the global structural information of activities, using their local event statistics. We demonstrate how maximal cliques in an undirected edge-weighted graph of activities, can be used for activity-class discovery in an unsupervised manner. We show how modeling an activity as a variable length Markov process, can be used to discover recurrent event-motifs to characterize the discovered activity-classes. We present results over extensive data-sets, collected from multiple active environments, to show the competence and generalizability of our proposed framework.
Other Publication Sites
A few more sites that aggregate research publications: Academic.edu, Bibsonomy, CiteULike, Mendeley.
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