Joint Segmentation and Classification of actions using a Conditional Random Field

被引：0

作者：

Kosmopoulos, Dimitrios ^{[1
]}

Maglogiannis, Ilias ^{[2
]}

机构：

[1] Univ Patras, Agrinion 30100, Greece

[2] Univ Piraeus, Piraeus 18532, Greece

来源：

8TH ACM INTERNATIONAL CONFERENCE ON PERVASIVE TECHNOLOGIES RELATED TO ASSISTIVE ENVIRONMENTS (PETRA 2015) | 2015年

关键词：

conditional random fields; classification; segmentation;

D O I：

10.1145/2769493.2769586

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, we present results of joint segmentation and classification of sequences in the framework of conditional random field (CRF) models. We use a recently proposed dual-functionality CRF model: on the first level, the proposed model conducts sequence segmentation, while, on the second level, the whole observed sequences are classified into one of the available learned classes. We evaluate the efficacy of our approach considering a real-world application, and we compare its performance to popular alternatives.

引用

页数：4

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