NDLI: Spectral learning of latent semantics for action recognition

Content Provider	IEEE Xplore Digital Library
Author	Zhiwu Lu Yuxin Peng Ip, H.H.S.
Copyright Year	2011
Description	Author affiliation: Institute of Computer Science and Technology, Peking University, Beijing 100871, China (Zhiwu Lu; Yuxin Peng) \|\| Department of Computer Science, City University of Hong Kong, Hong Kong (Ip, H.H.S.)
Abstract	This paper proposes novel spectral methods for learning latent semantics (i.e. high-level features) from a large vocabulary of abundant mid-level features (i.e. visual keywords), which can help to bridge the semantic gap in the challenging task of action recognition. To discover the manifold structure hidden among mid-level features, we develop spectral embedding approaches based on graphs and hypergraphs, without the need to tune any parameter for graph construction which is a key step of manifold learning. In particular, the traditional graphs are constructed by linear reconstruction with sparse coding. In the new embedding space, we learn high-level latent semantics automatically from abundant mid-level features through spectral clustering. The learnt latent semantics can be readily used for action recognition with SVM by defining a histogram intersection kernel. Different from the traditional latent semantic analysis based on topic models, our two spectral methods for semantic learning can discover the manifold structure hidden among mid-level features, which results in compact but discriminative high-level features. The experimental results on two standard action datasets have shown the superior performance of our spectral methods.
Starting Page	1503
Ending Page	1510
File Size	253144
Page Count	8
File Format	PDF
ISBN	9781457711015
ISSN	15505499
e-ISBN	9781457711022
DOI	10.1109/ICCV.2011.6126408
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-06
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Semantics Feature extraction Manifolds Encoding Vocabulary Support vector machines Histograms
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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