short-paper

Leveraging Knowledge-based Inference for Material Classification

Authors:

Jie Yu,

Sandra Skaff,

Liang Peng,

Francisco ImaiAuthors Info & Claims

MM '15: Proceedings of the 23rd ACM international conference on Multimedia

Pages 1243 - 1246

https://doi.org/10.1145/2733373.2806327

Published: 13 October 2015 Publication History

Get Access

Abstract

Material classification is one of the fundamental problems for multimedia content analysis, computer vision and graphics. Existing efforts mostly focus on extracting representative visual features and training a classifier to recognize unknown materials. Compared with human visual recognition, automatic recognition cannot leverage common sense knowledge regarding material categories and contextual information such as object and scene. In this paper, we propose to first extract such knowledge on material, object and scene from heterogeneous sources, i.e. a public data set of 100 million Flickr images [13] and Bing search results. To improve the material classification task, the knowledge information is further exploited in a probabilistic inference framework. Our method is evaluated on OpenSurfaces [10], the largest public material data set which contains both visual features of physical properties as well as image context information. The quantitative evaluation demonstrates the superior performance of our proposed method.

References

[1]

Leung, T. and Malik, J., 1999. Recognizing surface using three-dimensional textons, International Conf. on Computer Vision.

Digital Library

Google Scholar

[2]

Heikkilä, M. and Pietikäinen, M., 2006. A texture-based method for modeling the background and detecting moving objects, IEEE Trans. Pattern Analysis and Machine Intelligence.

Digital Library

Google Scholar

[3]

Li, W., and Fritz, M., 2014. Learning multi-scale representations for material classification, arXiv preprint arXiv:1408.2938v1

Google Scholar

[4]

Murphy, K., Torrolba, A., and Freeman, W., 2003. Using the forest to see the trees: a graphical model relating features, objects, and scenes, Advances in Neural Information Processing Systems.

Google Scholar

[5]

Crandall, D. and Huttenlocher, D., 2007. Composite models of objects and scenes for category recognition, IEEE International Conf. on Computer Vision and Pattern Recognition.

Google Scholar

[6]

Li, J., Su, H., Lim, Y., and Li, F., 2010. Objects as attributes for scene classification, European Computer Vision Conference.

Digital Library

Google Scholar

[7]

Li, J. and Li, F., 2007. What, where and who? Classifying events by scene and object recognition, IEEE International Conf. on Computer Vision.

Google Scholar

[8]

Li, X. and Guo, Y., 2012. An object co-occurrence assisted hierarchical model for scene understanding, British Machine Vision Conference.

Google Scholar

[9]

Xie, L., and He, X., 2013. Picture tags and world knowledge: Learning tag relations from visual semantic sources, ACM International Conference on Multimedia.

Digital Library

Google Scholar

[10]

Bell, S., Upchurch, P., Snavely, N., and Bala, K., 2013. OpenSurfaces: a richly annotated catalog of surface appearance, ACM Transactions on Graphics.

Digital Library

Google Scholar

[11]

Bishop, C., 2006. Pattern Recognition and Machine Learning.

Digital Library

Google Scholar

[12]

Weiss, Y. and Freeman, W., 2001. Correctness of belief propagation in Gaussian graphical models of arbitrary topology, Neural Computation.

Digital Library

Google Scholar

[13]

Thomee, B., Shamma, D., Friedland, G., Elizalde, B., Ni, K., Poland, D., Borth, D. and Li, J., 2015, The new data and new challenges in multimedia research, arXiv preprint arXiv:1503.01817.

Google Scholar

Cited By

View all

Sun HYang J(2019)Domain-Specific Image Classification Using Ensemble Learning Utilizing Open-Domain Knowledge2019 International Conference on Computing, Networking and Communications (ICNC)10.1109/ICCNC.2019.8685507(593-596)Online publication date: Feb-2019
https://doi.org/10.1109/ICCNC.2019.8685507
Peng L(2019)Pixel2Field: Single Image Transformation to Physical Field of Sports VideosAdvances in Visual Computing10.1007/978-3-030-33720-9_51(661-669)Online publication date: 21-Oct-2019
https://doi.org/10.1007/978-3-030-33720-9_51
Lv ZPenades VBlasco SChirivella JGagliardo P(2017)Intuitively Evaluating Balance Measurement Software Using Kinect2ICTs for Improving Patients Rehabilitation Research Techniques10.1007/978-3-319-69694-2_8(83-93)Online publication date: 14-Nov-2017
https://doi.org/10.1007/978-3-319-69694-2_8
Show More Cited By

Index Terms

Leveraging Knowledge-based Inference for Material Classification
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information systems applications
    1. Data mining
    2. Multimedia information systems
      1. Multimedia databases

Recommendations

A study of neural-network-based classifiers for material classification

In this paper, the performance of the commonly used neural-network-based classifiers is investigated on solving a classification problem which aims to identify the object nature based on surface features of the object. When the surface data is obtained, ...
Marginal and simultaneous predictive classification using stratified graphical models

An inductive probabilistic classification rule must generally obey the principles of Bayesian predictive inference, such that all observed and unobserved stochastic quantities are jointly modeled and the parameter uncertainty is fully acknowledged ...
Accurate facial feature localization on expressional face images based on a graphical model approach
PCM'10: Proceedings of the Advances in multimedia information processing, and 11th Pacific Rim conference on Multimedia: Part II

Recent emergent face-related applications, such as face recognition and facial expression recognition, usually rely on accurate facial feature point localization. However, the variations in facial appearance, especially due to expressions, often make ...

Comments

Information & Contributors

Information

Published In

MM '15: Proceedings of the 23rd ACM international conference on Multimedia

October 2015

1402 pages

ISBN:9781450334594

DOI:10.1145/2733373

General Chairs:
Xiaofang Zhou
The University of Queensland, Australia
,
Alan F. Smeaton
Dublin City University, Ireland
,
Qi Tian
The University of Texas at San Antonio, USA
,
Program Chairs:
Dick C.A. Bulterman
FXPAL, USA
,
Heng Tao Shen
The University of Queensland, Australia
,
Ketan Mayer-Patel
The University of North Carolina, USA
,
Shuicheng Yan
National University of Singapore, Singapore

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 October 2015

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Conference

MM '15

Sponsor:

SIGMM

MM '15: ACM Multimedia Conference

October 26 - 30, 2015

Brisbane, Australia

Acceptance Rates

MM '15 Paper Acceptance Rate 56 of 252 submissions, 22%;

Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
178
Total Downloads

Downloads (Last 12 months)3
Downloads (Last 6 weeks)0

Reflects downloads up to 27 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Sun HYang J(2019)Domain-Specific Image Classification Using Ensemble Learning Utilizing Open-Domain Knowledge2019 International Conference on Computing, Networking and Communications (ICNC)10.1109/ICCNC.2019.8685507(593-596)Online publication date: Feb-2019
https://doi.org/10.1109/ICCNC.2019.8685507
Peng L(2019)Pixel2Field: Single Image Transformation to Physical Field of Sports VideosAdvances in Visual Computing10.1007/978-3-030-33720-9_51(661-669)Online publication date: 21-Oct-2019
https://doi.org/10.1007/978-3-030-33720-9_51
Lv ZPenades VBlasco SChirivella JGagliardo P(2017)Intuitively Evaluating Balance Measurement Software Using Kinect2ICTs for Improving Patients Rehabilitation Research Techniques10.1007/978-3-319-69694-2_8(83-93)Online publication date: 14-Nov-2017
https://doi.org/10.1007/978-3-319-69694-2_8
Lv ZPenades VBlasco SChirivella JGagliardo P(2017)Using Wii Balance Board to Evaluate Software Based on Kinect2ICTs for Improving Patients Rehabilitation Research Techniques10.1007/978-3-319-69694-2_6(59-68)Online publication date: 14-Nov-2017
https://doi.org/10.1007/978-3-319-69694-2_6

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Index Terms

Recommendations

A study of neural-network-based classifiers for material classification

Marginal and simultaneous predictive classification using stratified graphical models

Accurate facial feature localization on expressional face images based on a graphical model approach

Comments

Information

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations