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Pregled bibliografske jedinice broj: 774588

LMNN metric learning and fuzzy nearest neighbour classifier for hand gesture recognition


Marasović, Tea; Papić, Vladan; Zanchi, Vlasta
LMNN metric learning and fuzzy nearest neighbour classifier for hand gesture recognition // Journal on Multimodal User Interfaces, 9 (2015), 3; 211-221 doi:10.1007/s12193-015-0194-3 (međunarodna recenzija, članak, znanstveni)


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Naslov
LMNN metric learning and fuzzy nearest neighbour classifier for hand gesture recognition

Autori
Marasović, Tea ; Papić, Vladan ; Zanchi, Vlasta

Izvornik
Journal on Multimodal User Interfaces (1783-7677) 9 (2015), 3; 211-221

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
accelerometer; gesture recognition; mobile devices; human–computer interaction

Sažetak
This paper presents a novel gesture recognition system using a single three-axis accelerometer, that is to serve as an alternative or supplementary interaction modality for controlling mobile devices. Capturing, training and classification of the detected hand gestures are expected to be executed in their entirety on the mobile device running the proposed system, instead of being passed to a nearby computer. As gesture recognition belongs to the group of pattern recognition problems where the underlying class probabilities are not a priori known, the classification is based on the distance between neighbouring examples. The distance metric is optimized by using large margin nearest neighbour (LMNN) method. To measure the amount of classification confidence, a fuzzy version of nearest neighbour algorithm is employed. Obtained results for recognition of nine hand gestures using proposed LMNN—fuzzy combination are presented and compared to that of other similar approaches. The system achieves near perfect recognition accuracy that is highly competitive with systems based on statistical methods and other accelerometer-based gesture recognition systems in the literature.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo



POVEZANOST RADA


Ustanove:
Fakultet elektrotehnike, strojarstva i brodogradnje, Split

Profili:

Avatar Url Vladan Papić (autor)

Avatar Url Vlasta Zanchi (autor)

Avatar Url Tea Marasović (autor)

Citiraj ovu publikaciju

Marasović, Tea; Papić, Vladan; Zanchi, Vlasta
LMNN metric learning and fuzzy nearest neighbour classifier for hand gesture recognition // Journal on Multimodal User Interfaces, 9 (2015), 3; 211-221 doi:10.1007/s12193-015-0194-3 (međunarodna recenzija, članak, znanstveni)
Marasović, T., Papić, V. & Zanchi, V. (2015) LMNN metric learning and fuzzy nearest neighbour classifier for hand gesture recognition. Journal on Multimodal User Interfaces, 9 (3), 211-221 doi:10.1007/s12193-015-0194-3.
@article{article, year = {2015}, pages = {211-221}, DOI = {10.1007/s12193-015-0194-3}, keywords = {accelerometer, gesture recognition, mobile devices, human–computer interaction}, journal = {Journal on Multimodal User Interfaces}, doi = {10.1007/s12193-015-0194-3}, volume = {9}, number = {3}, issn = {1783-7677}, title = {LMNN metric learning and fuzzy nearest neighbour classifier for hand gesture recognition}, keyword = {accelerometer, gesture recognition, mobile devices, human–computer interaction} }
@article{article, year = {2015}, pages = {211-221}, DOI = {10.1007/s12193-015-0194-3}, keywords = {accelerometer, gesture recognition, mobile devices, human–computer interaction}, journal = {Journal on Multimodal User Interfaces}, doi = {10.1007/s12193-015-0194-3}, volume = {9}, number = {3}, issn = {1783-7677}, title = {LMNN metric learning and fuzzy nearest neighbour classifier for hand gesture recognition}, keyword = {accelerometer, gesture recognition, mobile devices, human–computer interaction} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


Uključenost u ostale bibliografske baze podataka:


  • INSPEC
  • Scopus
  • Journal Citation Reports - Science Edition
  • Computing and Technology
  • Google Scholar


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