NDLI: Modeling spatio-temporal layout with Lie Algebrized Gaussians for action recognition

Content Provider	Springer Nature Link
Author	Chen, Meng Gong, Liyu Wang, Tianjiang Liu, Fang Feng, Qi
Copyright Year	2015
Abstract	We propose a novel approach to model spatio-temporal distribution of local features for action recognition in videos. The proposed approach is based on the Lie Algebrized Gaussians (LAG) which is a feature aggregation approach and yields high-dimensional video signature. In the framework of LAG, local features extracted from a video are aggregated to train a video-specific Gaussian Mixture Model (GMM). Then the video-specific GMM is encoded as a vector based on Lie group theory and this step is also referred to as GMM vectorization. As the video-specific GMM gives a soft partition of the feature space, for each cell of the feature space (i.e. each Gaussian component), we use a GMM to model the spatio-temporal locations of the local features assigned to the Gaussian component. The location GMMs are encoded as vectors just like the local feature GMM. We term those vectors of location GMMs spatio-temporal LAG (STLAG). In addition, although the LAG and the popular Fisher Vector (FV) are derived from distinct theory perspectives, we find that they are closely related. Hence the power and ℓ $_{2}$ normalization proposed for the FV are also beneficial to the LAG. Experimental results show that STLAG is very effective to model spatio-temporal layout compared with other techniques such as spatio-temporal pyramid and feature augmentation. Using the state-of-the-art dense trajectory features, our approach achieves state-of-the-art performance on two challenging datasets: Hollywood2 and HMDB51.
Starting Page	10335
Ending Page	10355
Page Count	21
File Format	PDF
ISSN	13807501
Journal	Multimedia Tools and Applications
Volume Number	75
Issue Number	17
e-ISSN	15737721
Language	English
Publisher	Springer US
Publisher Date	2015-10-26
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Action recognition Local spatio-temporal feature Gaussian Mixture Model Lie Algebrized Gaussians Spatio-temporal layout Multimedia Information Systems Computer Communication Networks Data Structures, Cryptology and Information Theory Special Purpose and Application-Based Systems
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Hardware and Architecture Software Media Technology

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Action recognition using lie algebrized gaussians over dense local spatio-temporal features

Effective human age estimation using a two-stage approach based on Lie Algebrized Gaussians feature

An effective head pose estimation approach using Lie Algebrized Gaussians based face representation

Projection transform on spatio-temporal context for action recognition

Robust human action recognition scheme based on high-level feature fusion

Human action recognition using modified slow feature analysis and multiple kernel learning

Spatio-Temporal Tube data representation and Kernel design for SVM-based video object retrieval system

Moving object detection in the encrypted domain

Temporal modulation normalization for robust speech feature extraction and recognition

Modeling spatio-temporal layout with Lie Algebrized Gaussians for action recognition

Similar Documents

Action recognition using lie algebrized gaussians over dense local spatio-temporal features

Effective human age estimation using a two-stage approach based on Lie Algebrized Gaussians feature

An effective head pose estimation approach using Lie Algebrized Gaussians based face representation

Projection transform on spatio-temporal context for action recognition

Robust human action recognition scheme based on high-level feature fusion

Human action recognition using modified slow feature analysis and multiple kernel learning

Spatio-Temporal Tube data representation and Kernel design for SVM-based video object retrieval system

Moving object detection in the encrypted domain

Temporal modulation normalization for robust speech feature extraction and recognition

Modeling spatio-temporal layout with Lie Algebrized Gaussians for action recognition