Similarity Searching in Long Sequences of Motion Capture Data
| Autor: | Petr Elias, Pavel Zezula, Jan Sedmidubský |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Matching (graph theory)
business.industry Nearest neighbor search 020207 software engineering Pattern recognition 02 engineering and technology Similarity measure Motion capture Similarity (network science) Subsequence 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Segmentation Artificial intelligence business Blossom algorithm Mathematics |
| Zdroj: | Similarity Search and Applications ISBN: 9783319467580 SISAP |
| DOI: | 10.1007/978-3-319-46759-7_21 |
| Popis: | Motion capture data digitally represent human movements by sequences of body configurations in time. Searching in such spatio-temporal data is difficult as query-relevant motions can vary in lengths and occur arbitrarily in the very long data sequence. There is also a strong requirement on effective similarity comparison as the specific motion can be performed by various actors in different ways, speeds or starting positions. To deal with these problems, we propose a new subsequence matching algorithm which uses a synergy of elastic similarity measure and multi-level segmentation. The idea is to generate a minimum number of overlapping data segments so that there is at least one segment matching an arbitrary subsequence. A non-partitioned query is then efficiently evaluated by searching for the most similar segments in a single level only, while guaranteeing a precise answer with respect to the similarity measure. The retrieval process is efficient and scalable which is confirmed by experiments executed on a real-life dataset. |
| Databáze: | OpenAIRE |
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