Action recognition based on learnt motion semantic vocabulary

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)Not applicablepeer-review

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Author(s)

Detail(s)

Original languageEnglish
Title of host publicationAdvances in Multimedia Information Processing, PCM 2010
Subtitle of host publication11th Pacific Rim Conference on Multimedia, Proceedings
PublisherSpringer Verlag
Pages193-202
Volume6297 LNCS
EditionPART 1
ISBN (Print)3642157017, 9783642157011
Publication statusPublished - 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6297 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Title11th Pacific Rim Conference on Multimedia, PCM 2010
PlaceChina
CityShanghai
Period21 - 24 September 2010

Abstract

This paper presents a novel contextual spectral embedding (CSE) framework for human action recognition, which automatically learns the high-level features (motion semantic vocabulary) from a large vocabulary of abundant mid-level features (i.e. visual words). Our novelty is to exploit the inter-video context between mid-level features for spectral embedding, while the context is captured by the Pearson product moment correlation between mid-level features instead of Gaussian function computed over the vectors of point-wise information as mid-level feature representation. Our goal is to embed the mid-level features into a semantic low-dimensional space, and learn a much compact semantic vocabulary upon the CSE framework. Experiments on two action datasets demonstrate that our approach can achieve significantly improved results with respect to the state of the arts. © 2010 Springer-Verlag.

Citation Format(s)

Action recognition based on learnt motion semantic vocabulary. / Zhao, Qiong; Lu, Zhiwu; Ip, Horace H. S.

Advances in Multimedia Information Processing, PCM 2010: 11th Pacific Rim Conference on Multimedia, Proceedings. Vol. 6297 LNCS PART 1. ed. Springer Verlag, 2010. p. 193-202 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6297 LNCS).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)Not applicablepeer-review