short-paper

Image classification using the web graph

Authors:

Dhruv Kumar Mahajan,

Malcolm SlaneyAuthors Info & Claims

MM '10: Proceedings of the 18th ACM international conference on Multimedia

Pages 991 - 994

https://doi.org/10.1145/1873951.1874131

Published: 25 October 2010 Publication History

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Abstract

Image classification is a well-studied and hard problem in computer vision. We extend a proven solution for classifying web spam to handle images. We exploit the link structure of the web graph: a web page related to a given category is normally linked to other pages describing related objects. Our approach combines information from the webgraph structure with semi-supervised learning from all the unlabeled images to create a superior image-classification model for multimedia data. We show that fusing image, text and web-graph features gives a 12% improvement (in the area under the ROC curve) over content features alone in an adult image-classification experiment.

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Cited By

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Cheng YZhu SLiang ZXu G(2016)Image classification via multi-view model2016 Chinese Control and Decision Conference (CCDC)10.1109/CCDC.2016.7531558(3333-3337)Online publication date: May-2016
https://doi.org/10.1109/CCDC.2016.7531558
Kim SSohn K(2015)Graph-Based Spam Image Detection for Mobile Phone Spam Image FilteringInternational Journal of Software Innovation10.4018/IJSI.20151001063:4(72-86)Online publication date: 1-Oct-2015
https://dl.acm.org/doi/10.4018/IJSI.2015100106
Sun CZhu SShi Z(2014)Improving Image Classification Quality Using Multi-View LearningAdvanced Materials Research10.4028/www.scientific.net/AMR.1049-1050.14751049-1050(1475-1479)Online publication date: Oct-2014
https://doi.org/10.4028/www.scientific.net/AMR.1049-1050.1475
Show More Cited By

Index Terms

Image classification using the web graph
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees

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Information

Published In

MM '10: Proceedings of the 18th ACM international conference on Multimedia

October 2010

1836 pages

ISBN:9781605589336

DOI:10.1145/1873951

General Chairs:
Alberto del Bimbo
University of Florence, Italy
,
Shih-Fu Chang
Columbia University, USA
,
Program Chair:
Arnold Smeulders
University of Amsterdam, NL

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 October 2010

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MM '10

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SIGMM

MM '10: ACM Multimedia Conference

October 25 - 29, 2010

Firenze, Italy

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Overall Acceptance Rate 995 of 4,171 submissions, 24%

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Cited By

View all

Cheng YZhu SLiang ZXu G(2016)Image classification via multi-view model2016 Chinese Control and Decision Conference (CCDC)10.1109/CCDC.2016.7531558(3333-3337)Online publication date: May-2016
https://doi.org/10.1109/CCDC.2016.7531558
Kim SSohn K(2015)Graph-Based Spam Image Detection for Mobile Phone Spam Image FilteringInternational Journal of Software Innovation10.4018/IJSI.20151001063:4(72-86)Online publication date: 1-Oct-2015
https://dl.acm.org/doi/10.4018/IJSI.2015100106
Sun CZhu SShi Z(2014)Improving Image Classification Quality Using Multi-View LearningAdvanced Materials Research10.4028/www.scientific.net/AMR.1049-1050.14751049-1050(1475-1479)Online publication date: Oct-2014
https://doi.org/10.4028/www.scientific.net/AMR.1049-1050.1475
Zhu SLiu JHu R(2014)A novel approach for image classificationThe 26th Chinese Control and Decision Conference (2014 CCDC)10.1109/CCDC.2014.6852938(4313-4318)Online publication date: May-2014
https://doi.org/10.1109/CCDC.2014.6852938
Marcos Alvarez AYamada MKimura ACesar PCooper MShamma DWilliams D(2013)Exploiting socially-generated side information in dimensionality reductionProceedings of the 2nd international workshop on Socially-aware multimedia10.1145/2509916.2509923(9-12)Online publication date: 21-Oct-2013
https://dl.acm.org/doi/10.1145/2509916.2509923
Zhang JSong YLeung TRamzan NWang FPatrikakis CCui PDoulamis NYang SSun G(2011)Improving video classification via youtube video co-watch dataProceedings of the 2011 ACM workshop on Social and behavioural networked media access10.1145/2072627.2072635(21-26)Online publication date: 1-Dec-2011
https://dl.acm.org/doi/10.1145/2072627.2072635
Zhong SLiu YLiu YCandan KPanchanathan SPrabhakaran BSundaram HFeng WSebe N(2011)Bilinear deep learning for image classificationProceedings of the 19th ACM international conference on Multimedia10.1145/2072298.2072344(343-352)Online publication date: 28-Nov-2011
https://dl.acm.org/doi/10.1145/2072298.2072344
Yew JShamma DChurchill ETan DFitzpatrick GGutwin CBegole BKellogg W(2011)Knowing funnyProceedings of the SIGCHI Conference on Human Factors in Computing Systems10.1145/1978942.1978984(297-306)Online publication date: 7-May-2011
https://dl.acm.org/doi/10.1145/1978942.1978984
Fields BJacobson KRhodes Cd'Inverno MSandler MCasey M(2011)Analysis and Exploitation of Musician Social Networks for Recommendation and DiscoveryIEEE Transactions on Multimedia10.1109/TMM.2011.211136513:4(674-686)Online publication date: 1-Aug-2011
https://dl.acm.org/doi/10.1109/TMM.2011.2111365

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