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HoP: Histogram of Patterns for Human Action Representation

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 10484)

Abstract

This paper presents a novel method for representing actions in terms of multinomial distributions of frequent sequential patterns of different length. Frequent sequential patterns are series of data descriptors that occur many times in the data. This paper proposes to learn a codebook of frequent sequential patterns by means of an apriori-like algorithm, and to represent an action with a Bag-of-Frequent-Sequential-Patterns approach. Preliminary experiments of the proposed method have been conducted for action classification on skeletal data. The method achieves state-of-the-art accuracy value in cross-subject validation.

Keywords

Action classification Apriori algorithm Frequent pattern 

Notes

Acknowledgement

We are grateful to Mr. Giovanni Caruana for making available his implementation of the classic apriori algorithm, which he implemented in his Master thesis work at University of Palermo.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Universita’ degli Studi di PalermoPalermoItaly

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