Matching Seqlets : An Unsupervised Approach for Locality Preserving Sequence Matching
In this paper, we propose a novel unsupervised approach for sequence matching by explicitly accounting for the locality properties in the sequences. In contrast to conventional approaches that rely on frame-to-frame matching, we conduct matching using sequencelet or seqlet, a sub-sequence wherein the frames share strong similarities and are thus grouped together. The optimal seqlets and matching between them are learned jointly, without any supervision from users. The learned seqlets preserve the locality information at the scale of interest and resolve the ambiguities during matching, which are omitted by frame-based matching methods. We show that our proposed approach outperforms the state-of-the-art ones on datasets of different domains including human actions, facial expressions, speech, and character strokes.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:43 |
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| Enthalten in: |
IEEE transactions on pattern analysis and machine intelligence - 43(2021), 2 vom: 06. Feb., Seite 745-752 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Qiu, Jiayan [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 29.09.2021 Date Revised 29.09.2021 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1109/TPAMI.2019.2934052 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM300350457 |
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| 245 | 1 | 0 | |a Matching Seqlets |b An Unsupervised Approach for Locality Preserving Sequence Matching |
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| 520 | |a In this paper, we propose a novel unsupervised approach for sequence matching by explicitly accounting for the locality properties in the sequences. In contrast to conventional approaches that rely on frame-to-frame matching, we conduct matching using sequencelet or seqlet, a sub-sequence wherein the frames share strong similarities and are thus grouped together. The optimal seqlets and matching between them are learned jointly, without any supervision from users. The learned seqlets preserve the locality information at the scale of interest and resolve the ambiguities during matching, which are omitted by frame-based matching methods. We show that our proposed approach outperforms the state-of-the-art ones on datasets of different domains including human actions, facial expressions, speech, and character strokes | ||
| 650 | 4 | |a Journal Article | |
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| 700 | 1 | |a Fua, Pascal |e verfasserin |4 aut | |
| 700 | 1 | |a Tao, Dacheng |e verfasserin |4 aut | |
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