A Voting-Based Sequential Pattern Recognition Method

Koichi Ogawara, Masahiro Fukutomi, Seiichi Uchida, Yaokai Feng

研究成果: Contribution to journalArticle査読

2 被引用数 (Scopus)

抄録

We propose a novel method for recognizing sequential patterns such as motion trajectory of biological objects (i.e., cells, organelle, protein molecules, etc.), human behavior motion, and meteorological data. In the proposed method, a local classifier is prepared for every point (or timing or frame) and then the whole pattern is recognized by majority voting of the recognition results of the local classifiers. The voting strategy has a strong benefit that even if an input pattern has a very large deviation from a prototype locally at several points, they do not severely influence the recognition result; they are treated just as several incorrect votes and thus will be neglected successfully through the majority voting. For regularizing the recognition result, we introduce partial-dependency to local classifiers. An important point is that this dependency is introduced to not only local classifiers at neighboring point pairs but also to those at distant point pairs. Although, the dependency makes the problem non-Markovian (i.e., higher-order Markovian), it can still be solved efficiently by using a graph cut algorithm with polynomial-order computations. The experimental results revealed that the proposed method can achieve better recognition accuracy while utilizing the above characteristics of the proposed method.

本文言語英語
論文番号e76980
ジャーナルPloS one
8
10
DOI
出版ステータス出版済み - 10 14 2013

All Science Journal Classification (ASJC) codes

  • 生化学、遺伝学、分子生物学(全般)
  • 農業および生物科学(全般)
  • 一般

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