TY - GEN
T1 - Concept embedding through canonical forms
T2 - 25th International Conference on Pattern Recognition, ICPR 2020
AU - Kamzin, A.
AU - Amperayani, V. N.S.A.
AU - Sukhapalli, P.
AU - Banerjee, A.
AU - Gupta, S. K.S.
N1 - Publisher Copyright:
© 2020 IEEE
PY - 2020
Y1 - 2020
N2 - In the recognition problem, a canonical form that expresses the spatio-temporal relation of concepts for a given class can potentially increase accuracy. Concepts are defined as attributes that can be recognized using a soft matching paradigm. We consider the specific case study of American Sign Language (ASL) to show that canonical forms of classes can be used to recognize unseen gestures. There are several advantages of a canonical form of gestures including translation between gestures, gesture-based searching, and automated transcription of gestures into any spoken language. We applied our technique to two independently collected datasets: a) IMPACT Lab dataset: 23 ASL gestures each executed three times from 130 first time ASL learners as training data and b) ASLTEXT dataset: 190 gestures each executed six times on an average. Our technique was able to recognize 19 arbitrarily chosen previously unseen gestures in the IMPACT dataset from seven individuals who are not a part of 130 and 34 unseen gestures from the ASLTEXT dataset without any retraining. Our normalized accuracy on the ASLTEXT dataset is 66% which is 13.6 % higher than the state-of-art technique. Comparison with deep learning techniques revealed that incorporation of concept level knowledge can potentially alleviate under-fitting problems.
AB - In the recognition problem, a canonical form that expresses the spatio-temporal relation of concepts for a given class can potentially increase accuracy. Concepts are defined as attributes that can be recognized using a soft matching paradigm. We consider the specific case study of American Sign Language (ASL) to show that canonical forms of classes can be used to recognize unseen gestures. There are several advantages of a canonical form of gestures including translation between gestures, gesture-based searching, and automated transcription of gestures into any spoken language. We applied our technique to two independently collected datasets: a) IMPACT Lab dataset: 23 ASL gestures each executed three times from 130 first time ASL learners as training data and b) ASLTEXT dataset: 190 gestures each executed six times on an average. Our technique was able to recognize 19 arbitrarily chosen previously unseen gestures in the IMPACT dataset from seven individuals who are not a part of 130 and 34 unseen gestures from the ASLTEXT dataset without any retraining. Our normalized accuracy on the ASLTEXT dataset is 66% which is 13.6 % higher than the state-of-art technique. Comparison with deep learning techniques revealed that incorporation of concept level knowledge can potentially alleviate under-fitting problems.
UR - http://www.scopus.com/inward/record.url?scp=85110501587&partnerID=8YFLogxK
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U2 - 10.1109/ICPR48806.2021.9412207
DO - 10.1109/ICPR48806.2021.9412207
M3 - Conference contribution
AN - SCOPUS:85110501587
T3 - Proceedings - International Conference on Pattern Recognition
SP - 6157
EP - 6164
BT - Proceedings of ICPR 2020 - 25th International Conference on Pattern Recognition
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 10 January 2021 through 15 January 2021
ER -