NDLI: Mining semantic affordances of visual object categories

Content Provider	IEEE Xplore Digital Library
Author	Yu-Wei Chao Zhan Wang Mihalcea, R. Jia Deng
Copyright Year	2015
Description	Author affiliation: Comput. Sci. & Eng., Univ. of Michigan, Ann Arbor, MI, USA (Yu-Wei Chao; Zhan Wang; Mihalcea, R.; Jia Deng)
Abstract	Affordances are fundamental attributes of objects. Affordances reveal the functionalities of objects and the possible actions that can be performed on them. Understanding affordances is crucial for recognizing human activities in visual data and for robots to interact with the world. In this paper we introduce the new problem of mining the knowledge of semantic affordance: given an object, determining whether an action can be performed on it. This is equivalent to connecting verb nodes and noun nodes in WordNet, or filling an affordance matrix encoding the plausibility of each action-object pair. We introduce a new benchmark with crowdsourced ground truth affordances on 20 PASCAL VOC object classes and 957 action classes. We explore a number of approaches including text mining, visual mining, and collaborative filtering. Our analyses yield a number of significant insights that reveal the most effective ways of collecting knowledge of semantic affordances.
Starting Page	4259
Ending Page	4267
File Size	823515
Page Count	9
File Format	PDF
ISSN	10636919
e-ISBN	9781467369640
DOI	10.1109/CVPR.2015.7299054
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Semantics Visualization Feeds Videos Crowdsourcing Switches Acceleration
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

Visual Semantic Search: Retrieving Videos via Complex Textual Queries

Visual and semantic similarity in ImageNet

Mining Semantic Affordances of Visual Object Categories

TVSum: Summarizing web videos using titles

Eye tracking assisted extraction of attentionally important objects from videos

Histograms of Pattern Sets for Image Classification and Object Recognition

Inferring social relations from visual concepts

Object Discovery by Clustering Correlated Visual Word Sets

Zero-shot object recognition by semantic manifold distance

Mining semantic affordances of visual object categories

Similar Documents

Visual Semantic Search: Retrieving Videos via Complex Textual Queries

Visual and semantic similarity in ImageNet

Mining Semantic Affordances of Visual Object Categories

TVSum: Summarizing web videos using titles

Eye tracking assisted extraction of attentionally important objects from videos

Histograms of Pattern Sets for Image Classification and Object Recognition

Inferring social relations from visual concepts

Object Discovery by Clustering Correlated Visual Word Sets

Zero-shot object recognition by semantic manifold distance

Mining semantic affordances of visual object categories