NDLI: What, where and who? Classifying events by scene and object recognition

Content Provider	IEEE Xplore Digital Library
Author	Li-Jia Li Li Fei-Fei
Copyright Year	2007
Description	Author affiliation: Univ. of Illinois at Urbana-Champaign, Champaign (Li-Jia Li)
Abstract	We propose a first attempt to classify events in static images by integrating scene and object categorizations. We define an event in a static image as a human activity taking place in a specific environment. In this paper, we use a number of sport games such as snow boarding, rock climbing or badminton to demonstrate event classification. Our goal is to classify the event in the image as well as to provide a number of semantic labels to the objects and scene environment within the image. For example, given a rowing scene, our algorithm recognizes the event as rowing by classifying the environment as a lake and recognizing the critical objects in the image as athletes, rowing boat, water, etc. We achieve this integrative and holistic recognition through a generative graphical model. We have assembled a highly challenging database of 8 widely varied sport events. We show that our system is capable of classifying these event classes at 73.4% accuracy. While each component of the model contributes to the final recognition, using scene or objects alone cannot achieve this performance.
Starting Page	1
Ending Page	8
File Size	847829
Page Count	8
File Format	PDF
ISBN	9781424416301
ISSN	15505499
DOI	10.1109/ICCV.2007.4408872
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-10-14
Publisher Place	Brazil
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Layout Object recognition Image recognition Humans Snow Lakes Boats Graphical models Assembly Image databases
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
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

Probabilistic spatial context models for scene content understanding

Contour grouping with strong prior models

Learning hierarchical models of scenes, objects, and parts

Toward coherent object detection and scene layout understanding

What, where and who? classifying event by scene and object recognition (2007)

Nonparametric scene parsing: Label transfer via dense scene alignment

Combining attention and recognition for rapid scene analysis

Object-graphs for context-aware category discovery

A Sentence Is Worth a Thousand Pixels

What, where and who? Classifying events by scene and object recognition

Similar Documents

Probabilistic spatial context models for scene content understanding

Contour grouping with strong prior models

Learning hierarchical models of scenes, objects, and parts

Toward coherent object detection and scene layout understanding

What, where and who? classifying event by scene and object recognition (2007)

Nonparametric scene parsing: Label transfer via dense scene alignment

Combining attention and recognition for rapid scene analysis

Object-graphs for context-aware category discovery

A Sentence Is Worth a Thousand Pixels

What, where and who? Classifying events by scene and object recognition