About: Motion Words: A Text-Like Representation of 3D Skeleton Sequences

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Motion Words: A Text-Like Representation of 3D Skeleton Sequences
Creator	Budikova, Petra Dohnal, Vlastislav Sedmidubsky, Jan Zezula, Pavel
Source	PMC
abstract	There is a growing amount of human motion data captured as a continuous 3D skeleton sequence without any information about its semantic partitioning. To make such unsegmented and unlabeled data efficiently accessible, we propose to transform them into a text-like representation and employ well-known text retrieval models. Specifically, we partition each motion synthetically into a sequence of short segments and quantize the segments into motion words, i.e. compact features with similar characteristics as words in text documents. We introduce several quantization techniques for building motion-word vocabularies and propose application-independent criteria for assessing the vocabulary quality. We verify these criteria on two real-life application scenarios.
has issue date	2020-03-17(xsd:dateTime)
bibo:doi	10.1007/978-3-030-45439-5_35
has license	no-cc
sha1sum (hex)	f42c7f34a7ef3435167de83ba15adb90f700c008
schema:url	https://doi.org/10.1007/978-3-030-45439-5_35
resource representing a document's title	Motion Words: A Text-Like Representation of 3D Skeleton Sequences
has PubMed Central identifier	PMC7148217
schema:publication	Advances in Information Retrieval
resource representing a document's body	covid:f42c7f34a7ef3435167de83ba15adb90f700c008#body_text
is schema:about of	named entity 'motion' named entity 'There' named entity 'segments' named entity 'sequence' named entity 'human motion' named entity 'application' named entity 'TRANSFORM' named entity 'QUANTIZATION' named entity 'TECHNIQUES' named entity 'REPRESENTATION' named entity 'BUILDING' named entity 'CONTINUOUS' named entity 'LIFE' named entity 'DATA' named entity 'WORD' named entity 'verify' named entity 'semantic' named entity 'sequence' named entity 'motion data' named entity 'MWs' named entity 'text retrieval' named entity 'MWs' named entity 'semantic' named entity 'usability' named entity 'text-processing' named entity 'k-means clustering' named entity 'recursive' named entity 'orders of magnitude' named entity 'text documents' named entity 'D20' named entity 'k-means clustering' named entity 'baseline case' named entity 'kNN' named entity 'classifier' named entity 'k-medoids' named entity 'baseline case' named entity 'Axel jump' named entity 'precision and recall' named entity 'DTW' named entity 'Dynamic Time Warping' named entity 'n-tuple' named entity 'DTW' named entity 'explicit knowledge' named entity 'k-medoids' named entity 'text-retrieval' named entity 'global optimum' named entity 'semantic' named entity 'motion data' named entity 'ordered sequence' named entity 'algorithm' named entity 'motion data' named entity 'MWs' named entity 'inverted files' named entity 'k-medoids' named entity 'sampling frequency' named entity 'classifier' named entity 'text document' named entity 'DTW' named entity 'k-medoids' named entity 'DTW' named entity 'search indexes' named entity 'MWs' named entity 'MWs' named entity 'motion capture' named entity 'k-medoids' named entity 'neural-network' named entity 'k-medoids' named entity 'word stemming' named entity 'inverted file' named entity 'MWs' named entity 'figure skating competition' named entity 'indexability' named entity 'text retrieval' named entity 'unrelated segments' named entity 'selection algorithm' named entity 'cardinality' named entity 'MWs' named entity 'k-medoids' named entity 'motion data' named entity 'k-medoids'

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