research-article

Aerial scene recognition using efficient sparse representation

Author:

Anil CheriyadatAuthors Info & Claims

ICVGIP '12: Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing

Article No.: 11, Pages 1 - 8

https://doi.org/10.1145/2425333.2425344

Published: 16 December 2012 Publication History

Abstract

Advanced scene recognition systems for processing large volumes of high-resolution aerial image data are in great demand today. However, automated scene recognition remains a challenging problem. Efficient encoding and representation of spatial and structural patterns in the imagery are key in developing automated scene recognition algorithms. We describe an image representation approach that uses simple and computationally efficient sparse code computation to generate accurate features capable of producing excellent classification performance using linear SVM kernels. Our method exploits unlabeled low-level image feature measurements to learn a set of basis vectors. We project the low-level features onto the basis vectors and use simple soft threshold activation function to derive the sparse features. The proposed technique generates sparse features at a significantly lower computational cost than other methods [25, 27], yet it produces comparable or better classification accuracy. We apply our technique to high-resolution aerial image datasets to quantify the aerial scene classification performance. We demonstrate that the dense feature extraction and representation methods are highly effective for automatic large-facility detection on wide area high-resolution aerial imagery.

References

[1]

Y.-L. Boureau, F. Bach, Y. LeCun, and J. Ponce. Learning mid-level features for recognition. In IEEE Conference on Computer Vision and Pattern Recognition, pages 2559--2566, 2010.

[2]

L. Bruzzone and L. Carlin. A multilevel context-based system for classification of very high spatial resolution images. IEEE Transactions on Geoscience and Remote Sensing, 44(9): 2587--2600, sept. 2006.

[3]

C.-C. Chang and C.-J. Lin. LIBSVM: a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.

Digital Library

[4]

A. Coates and A. Y. Ng. The importance of encoding versus training with sparse coding and vector quantization. In International Conference on Machine Learning, volume 28, 2011.

[5]

L. Fei-Fei and P. Perona. A bayesian hierarchical model for learning natural scene categories. In Proc. of IEEE Conference on Computer Vision and Pattern Recognition, 2005.

Digital Library

[6]

J. C. V. Gemert, J. M. Geusebroek, C. J. Veenman, and A. W. M. Smeulders. Kernel codebooks for scene categorization. In Proc. of European Conference on Computer Vision, 2008.

Digital Library

[7]

K. Gregor and Y. LeCun. Learning fast approximations of sparse coding. In International Conference on Machine Learning, volume 27, 2010.

[8]

P. O. Hoyer. Non-negative matrix factorization with sparseness constraints. Journal of Machine Learning Research, 5: 1457--1469, 2004.

Digital Library

[9]

A. Hyvarinen and E. Oja. Independent component analysis: algorithms and applications. Neural Networks, 13(4--5): 411--430, 2000.

Digital Library

[10]

S. Lazebnik, C. Schmid, and J. Ponce. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In IEEE Conference on Computer Vision and Pattern Recognition, 2006.

Digital Library

[11]

H. Lee, A. Battle, R. Raina, and A. Y. Ng. Efficient sparse coding algorithms. In Proc. of Neural Information Processing Systems, 2006.

[12]

M. Lienou, H. Maitre, and M. Datcu. Semantic annotation of satellite images using Latent Dirichlet Allocation. IEEE Geoscience and Remote Sensing Letters, 7(1): 28--32, jan. 2010.

[13]

D. G. Lowe. Object recognition from local scale-invariant features. In Proceedings of the International Conference on Computer Vision, 1999.

Digital Library

[14]

A. Lu, X. Hou, C. Lin, and C.-L. Liu. Insect species recognition using sparse representation. In British Machine Vision Conference, pages 108.1--10, 2010.

[15]

Y. C. Pati, R. Rezaifar, and P. S. Krishnaprasad. Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition. In Asilomar Conference on Signals, Systems and Computers, 1993.

[16]

M. Pesaresi and A. Gerhardinger. Improved textural built-up presence index for automatic recognition of human settlements in arid regions with scattered vegetation. Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of, 4(1): 16--26, march 2011.

[17]

P. Quelhas, F. Monay, J.-M. Odobez, D. Gatica-Perez, T. Tuytelaars, and L. Van Gool. Modeling scenes with local descriptors and latent aspects. In IEEE International Conference on Computer Vision, volume 1, pages 883--890 Vol. 1, oct. 2005.

Digital Library

[18]

J. Sivic and A. Zisserman. Video google: A text retrieval approach to object matching in videfos. In Proc. of IEEE International Conference on Computer Vision, 2003.

Digital Library

[19]

R. Vatsavai, A. Cheriyadat, and S. Gleason. Unsupervised semantic labeling framework for identification of complex facilities in high-resolution remote sensing images. In IEEE International Conference on Data Mining Workshops (ICDMW), pages 273--280, dec. 2010.

Digital Library

[20]

A. Vedaldi and B. Fulkerson. VLFeat: An open and portable library of computer vision algorithms. http://www.vlfeat.org/, 2008.

[21]

J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong. Locality-constrained linear coding for image classification. In Proc. of IEEE Conference on Computer Vision and Pattern Recognition, 2010.

[22]

X. Wang and E. Grimson. Spatial latent dirichlet allocation. In Proc. of Neural Information Processing Systems, 2007.

[23]

J. Winn, A. Criminisi, and T. Minka. Object categorization by learned universal visual dictionary. In Proc. of IEEE International Conference on Computer Vision, 2005.

Digital Library

[24]

J. Wright, Y. Ma, J. Mairal, G. Sapiro, T. S. Huang, and Y. Shuicheng. Sparse representation for computer vision and pattern recognition. Proceedings of the IEEE, 98(6): 1031--1044, june 2010.

[25]

J. Yang, K. Yu, Y. Gong, and T. Huang. Linear spatial pyramid matching using sparse coding for image classification. In IEEE Conference on Computer Vision and Pattern Recognition, pages 1794--1801, june 2009.

[26]

L. Yang, R. Jin, R. Sukthankar, and F. Jurie. Unifying discriminative visual codebook generation with classifier training for object category recognition. In IEEE Conference on Computer Vision and Pattern Recognition, 2008.

[27]

Y. Yang and S. Newsam. Spatial pyramid co-occurrence for image classification. In IEEE International Conference on Computer Vision, pages 1465--1472, nov. 2011.

Digital Library

Cited By

Giriraja CHaswanth ASrinivasa CJayaRam TKrishnaiah PRangan PJayaraman B(2014)Satellite Image Classification Using Unsupervised Learning and SIFTProceedings of the 2014 International Conference on Interdisciplinary Advances in Applied Computing10.1145/2660859.2660920(1-6)Online publication date: 10-Oct-2014
https://dl.acm.org/doi/10.1145/2660859.2660920
Firat OCan GVural F(2014)Representation Learning for Contextual Object and Region Detection in Remote SensingProceedings of the 2014 22nd International Conference on Pattern Recognition10.1109/ICPR.2014.637(3708-3713)Online publication date: 24-Aug-2014
https://dl.acm.org/doi/10.1109/ICPR.2014.637
Firat OVural F(2013)Representation learning with convolutional sparse autoencoders for remote sensing2013 21st Signal Processing and Communications Applications Conference (SIU)10.1109/SIU.2013.6531525(1-4)Online publication date: Apr-2013
https://doi.org/10.1109/SIU.2013.6531525

Index Terms

Aerial scene recognition using efficient sparse representation
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision

Recommendations

Locality-sensitive kernel sparse representation classification for face recognition

A new classification method called LS-KSRC is proposed.LS-KSRC integrates both sparsity and data locality in the kernel feature space.LS-KSRC's closed form solution of the l1-norm minimization problem is presented.LS-KSRC outperforms KSRC, SRC, LLC, SVM,...
Robust face recognition using sparse representation in LDA space

In this article, we address the problem of face recognition under uncontrolled conditions. The proposed solution is a numerical robust algorithm dealing with face images automatically registered and projected via the linear discriminant analysis (LDA) ...
Loose L_1/2 regularised sparse representation for face recognition

Sparse representation (or sparse coding) has been applied to deal with frontal face recognition. Two representative methods are the sparse representation‐based classification (SRC) and the collaborative representation‐based classification (CRC), in which ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

ICVGIP '12: Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing

December 2012

633 pages

ISBN:9781450316606

DOI:10.1145/2425333

Program Chairs:
Bill Triggs
CNRS, France
,
Kavita Bala
Cornell University
,
Sharat Chandran
IIT Bombay, India

Copyright © 2012 ACM.

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: 16 December 2012

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

ICVGIP '12

ICVGIP '12: The Eighth Indian Conference on Vision, Graphics and Image Processing

December 16 - 19, 2012

Mumbai, India

Acceptance Rates

Overall Acceptance Rate 95 of 286 submissions, 33%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
176
Total Downloads

Downloads (Last 12 months)2
Downloads (Last 6 weeks)0

Reflects downloads up to 03 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Giriraja CHaswanth ASrinivasa CJayaRam TKrishnaiah PRangan PJayaraman B(2014)Satellite Image Classification Using Unsupervised Learning and SIFTProceedings of the 2014 International Conference on Interdisciplinary Advances in Applied Computing10.1145/2660859.2660920(1-6)Online publication date: 10-Oct-2014
https://dl.acm.org/doi/10.1145/2660859.2660920
Firat OCan GVural F(2014)Representation Learning for Contextual Object and Region Detection in Remote SensingProceedings of the 2014 22nd International Conference on Pattern Recognition10.1109/ICPR.2014.637(3708-3713)Online publication date: 24-Aug-2014
https://dl.acm.org/doi/10.1109/ICPR.2014.637
Firat OVural F(2013)Representation learning with convolutional sparse autoencoders for remote sensing2013 21st Signal Processing and Communications Applications Conference (SIU)10.1109/SIU.2013.6531525(1-4)Online publication date: Apr-2013
https://doi.org/10.1109/SIU.2013.6531525

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.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten