135,665 research outputs found
Pose sentences : a new representation for understanding human actions
Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2008.Thesis (Master's) -- Bilkent University, 2008.Includes bibliographical references leaves 55-58.In this thesis we address the problem of human action recognition from video sequences.
Our main contribution to the literature is the compact use of poses while
representing videos and most importantly considering actions as pose-sentences
and exploit string matching approaches for classification. We focus on single actions,
where the actor performs one simple action through the video sequence. We
represent actions as documents consisting of words, where a word refers to a pose
in a frame. We think pose information is a powerful source for describing actions.
In search of a robust pose descriptor, we make use of four well-known techniques
to extract pose information, Histogram of Oriented Gradients, k-Adjacent Segments,
Shape Context and Optical Flow Histograms. To represent actions, first
we generate a codebook which will act as a dictionary for our action dataset.
Action sequences are then represented using a sequence of pose-words, as posesentences.
The similarity between two actions are obtained using string matching
techniques. We also apply a bag-of-poses approach for comparison purposes and
show the superiority of pose-sentences. We test the efficiency of our method with
two widely used benchmark datasets, Weizmann and KTH. We show that pose is
indeed very descriptive while representing actions, and without having to examine
complex dynamic characteristics of actions, one can apply simple techniques
with equally successful results.Hatun, KardelenM.S
Tactical Generation in a Free Constituent Order Language
This paper describes tactical generation in Turkish, a free constituent order
language, in which the order of the constituents may change according to the
information structure of the sentences to be generated. In the absence of any
information regarding the information structure of a sentence (i.e., topic,
focus, background, etc.), the constituents of the sentence obey a default
order, but the order is almost freely changeable, depending on the constraints
of the text flow or discourse. We have used a recursively structured finite
state machine for handling the changes in constituent order, implemented as a
right-linear grammar backbone. Our implementation environment is the GenKit
system, developed at Carnegie Mellon University--Center for Machine
Translation. Morphological realization has been implemented using an external
morphological analysis/generation component which performs concrete morpheme
selection and handles morphographemic processes.Comment: gzipped, uuencoded postscript fil
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Proceedings of QG2010: The Third Workshop on Question Generation
These are the peer-reviewed proceedings of "QG2010, The Third Workshop on Question Generation". The workshop included a special track for "QGSTEC2010: The First Question Generation Shared Task and Evaluation Challenge".
QG2010 was held as part of The Tenth International Conference on Intelligent Tutoring Systems (ITS2010)
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Language acquisition and machine learning
In this paper, we review recent progress in the field of machine learning and examine its implications for computational models of language acquisition. As a framework for understanding this research, we propose four component tasks involved in learning from experience - aggregation, clustering, characterization, and storage. We then consider four common problems studied by machine learning researchers - learning from examples, heuristics learning, conceptual clustering, and learning macro-operators - describing each in terms of our framework. After this, we turn to the problem of grammar acquisition, relating this problem to other learning tasks and reviewing four AI systems that have addressed the problem. Finally, we note some limitations of the earlier work and propose an alternative approach to modeling the mechanisms underlying language acquisition
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