More Web Proxy on the site http://driver.im/

article

Region-of-interest extraction in low depth of field images using ensemble clustering and difference of Gaussian approaches

Authors:

W. L. WooAuthors Info & Claims

Pattern Recognition, Volume 46, Issue 10

Pages 2685 - 2699

https://doi.org/10.1016/j.patcog.2013.03.006

Published: 01 October 2013 Publication History

Abstract

In this paper, a two-stage unsupervised segmentation approach based on ensemble clustering is proposed to extract the focused regions from low depth-of-field (DOF) images. The first stage is to cluster image blocks in a joint contrast-energy feature space into three constituent groups. To achieve this, we make use of a normal mixture-based model along with standard expectation-maximization (EM) algorithm at two consecutive levels of block size. To avoid the common problem of local optima experienced in many models, an ensemble EM clustering algorithm is proposed. As a result, relevant blocks closely conforming to image objects are extracted. In stage two, a binary saliency map is constructed from the relevant blocks at the pixel level, which is based on difference of Gaussian (DOG) and binarization methods. Then, a set of morphological operations is employed to create the region-of-interest (ROI) from the map. Experimental results demonstrate that the proposed approach achieves an F-measure of 91.3% and is computationally 3 times faster than the existing state-of-the-art approach.

References

[1]

Tsai, D.-M. and Wang, H.-J., Segmenting focused objects in complex visual images. Pattern Recognition Letters. v19. 929-940.

Digital Library

[2]

Smeulders, A., Worring, W., Santini, S., Gupta, A. and Jain, R., Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence. v22. 1349-1380.

Digital Library

[3]

Datta, R., Joshi, D., Li, J. and Wang, J.Z., Image retrieval: ideas, influences, and trends of the new age. ACM Computing Surveys. v40. 1-60.

Digital Library

[4]

G. Rafiee, S. S. Dlay, and W. L. Woo, A review of content-based image retrieval, in: 7th International Symposium on Communication Systems Networks and Digital Signal Processing (CSNDSP), 2010, pp. 775-779.

[5]

Deng, Y. and Manjunath, B.S., Unsupervised segmentation of color-texture regions in images and video. IEEE Transactions on Pattern Analysis and Machine Intelligence. v23. 800-810.

Digital Library

[6]

Carson, C., Belongie, S., Greenspan, H. and Malik, J., Blobworld: image segmentation using expectation-maximization and its application to image querying. IEEE Transactions on Pattern Analysis and Machine Intelligence. v24. 1026-1038.

Digital Library

[7]

Tao, W., Jin, H. and Zhang, Y., Color image segmentation based on mean shift and normalized cuts. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics. v37. 1382-1389.

Digital Library

[8]

Garcia Ugarriza, L., Saber, E., Vantaram, S.R., Amuso, V., Shaw, M. and Bhaskar, R., Automatic image segmentation by dynamic region growth and multiresolution merging. IEEE Transactions on Image Processing. v18. 2275-2288.

Digital Library

[9]

Yu, S.X. and Shi, J., Segmentation given partial grouping constraints. IEEE Transactions on Pattern Analysis and Machine Intelligence. v26. 173-183.

Digital Library

[10]

Grady, L., Random walks for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence. v28. 1768-1783.

Digital Library

[11]

Wang, J.Z., Jia, L., Gray, R.M. and Wiederhold, G., Unsupervised multiresolution segmentation for images with low depth of field. IEEE Transactions on Pattern Analysis and Machine Intelligence. v23. 85-90.

Digital Library

[12]

Peng, B., Zhang, L., Zhang, D. and Yang, J., Image segmentation by iterated region merging with localized graph cuts. Pattern Recognition. v44. 2527-2538.

Digital Library

[13]

Adam, A., The Camera. 1980. New York Graphic Society, Boston.

[14]

Kim, C., Segmenting a low-depth-of-field image using morphological filters and region merging. IEEE Transactions on Image Processing. v14. 1503-1511.

Digital Library

[15]

Li, H. and Ngan, K.N., Unsupervized video segmentation with low depth of field. IEEE Transactions on Circuits and Systems for Video Technology. v17. 1742-1751.

Digital Library

[16]

Li, H. and Ngan, K.N., Learning to extract focused objects from low dof images. IEEE Transactions on Circuits and Systems for Video Technology. v21. 1571-1580.

Digital Library

[17]

F. Graf, H. P. Kriegel, and M. Weiler, Robust segmentation of relevant regions in low depth of field images, in: IEEE International Conference on Image Processing, 2011, pp. 2861-2864.

[18]

Chen, T. and Li, H., Segmenting focused objects based on the amplitude decomposition model. Pattern Recognition Letters. v33. 1536-1542.

Digital Library

[19]

Kovacs, L. and Sziranyi, T., Focus area extraction by blind deconvolution for defining regions of interest. IEEE Transactions on Pattern Analysis and Machine Intelligence. v29. 1080-1085.

Digital Library

[20]

R. Liu, Z. Li, J. Jia, Image partial blur detection and classification, in: IEEE Conference on Computer Vision and Pattern Recognition, 2008, pp. 1-8.

[21]

Unser, M., Texture classification and segmentation using wavelet frames. IEEE Transactions on Image Processing. v4. 1549-1560.

Digital Library

[22]

Daubechies, I., Ten Lectures on Wavelets. 1992. Capital City Press.

Digital Library

[23]

Li, J. and Wang, J.Z., Automatic Linguistic Indexing of Pictures by a statistical modeling approach. IEEE Transactions on Pattern Analysis and Machine Intelligence. v25. 1075-1088.

Digital Library

[24]

Dempster, A.P., Laird, N.M. and Rubin, D.B., Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. v34B. 1-38.

[25]

Figueiredo, M.A.T. and Jain, A.K., Unsupervised learning of finite mixture models. IEEE Transactions on Pattern Analysis and Machine Intelligence. v24. 381-396.

Digital Library

[26]

Manning, D. and Schütze, H., Foundations of Statistical Natural Language Processing. 1999. The MIT Press, Cambridge, MA.

Digital Library

[27]

Gan, G., Ma, C. and Wu, J., Data Clustering: Theory, Algorithms, and Applications (ASA-SIAM Series on Statistics and Applied Probability). 2007. Society for Industrial and Applied Mathematics.

Digital Library

[28]

Kuncheva, L.I., Combining Pattern Classifiers, Methods and Algorithms. 2004. John Wiley and Sons.

Digital Library

[29]

Strehl, A. and Ghosh, J., Cluster ensembles - a knowledge reuse framework for combining multiple partitions. Journal of Machine Learning Research. v3. 583-617.

Digital Library

[30]

A. L. N. Fred and A. K. Jain, Data clustering using evidence accumulation, in 16th International Conference on Pattern Recognition, 2002, vol.4, pp. 276-280 .

[31]

Topchy, A., Jain, A.K. and Punch, W., Clustering ensembles: models of consensus and weak partitions. IEEE Transactions on Pattern Analysis and Machine Intelligence. v27. 1866-1881.

Digital Library

[32]

A. L. N. Fred and A. K. Jain, Robust data clustering, in: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003, vol. 2, pp. 128-33.

[33]

D. G. Lowe, Object recognition from local scale-invariant features, in: The Proceedings of the Seventh IEEE International Conference on Computer Vision, 1999, vol. 2, pp. 1150-1157.

Digital Library

[34]

R. Achanta, S. Hemami, F. Estrada, S. Susstrunk, Frequency-tuned salient region detection, in: IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 1597-1604.

[35]

Otsu, N., A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man and Cybernetics. v9. 62-66.

[36]

Trier, O.D. and Jain, A.K., Goal-directed evaluation of binarization methods. IEEE Transactions on Pattern Analysis and Machine Intelligence. v17. 1191-1201.

Digital Library

[37]

Gonzalez, R.C. and Woods, R.E., Digital Image Processing. 2002. Prentice Hall.

Digital Library

[38]

Pratt, W.K., Digital Image Processing. 2007. 4th edition. John Wiley & Sons, Inc.

Digital Library

[39]

Van Rijsbergen, C., Information Retrieval. 1979. second ed. Department of Computer Science, University of Glasgow.

Digital Library

[40]

Martin, D.R., Fowlkes, C.C. and Malik, J., Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Transactions on Pattern Analysis and Machine Intelligence. v26. 530-549.

Digital Library

Cited By

Qin ZFu QPeng J(2024)A computationally efficient and robust looming perception model based on dynamic neural fieldNeural Networks10.1016/j.neunet.2024.106502179:COnline publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1016/j.neunet.2024.106502
Shan YLi SLi FCui YLi SChen MHe X(2024)Fuzzy self-consistent clustering ensembleApplied Soft Computing10.1016/j.asoc.2023.111151151:COnline publication date: 17-Apr-2024
https://dl.acm.org/doi/10.1016/j.asoc.2023.111151
Shi YYang KYu ZChen CZeng H(2023)Adaptive Ensemble Clustering With Boosting BLS-Based AutoencoderIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.327112035:12(12369-12383)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1109/TKDE.2023.3271120
Show More Cited By

Region-of-interest extraction in low depth of field images using ensemble clustering and difference of Gaussian approaches
1. Computing methodologies

Recommendations

Ensemble-Initialized k-Means Clustering
ICMLC '19: Proceedings of the 2019 11th International Conference on Machine Learning and Computing

As one of the most classical clustering techniques, the k-means clustering has been widely used in various areas over the past few decades. Despite its significant success, there are still several challenging issues in the k-means clustering research, ...
A fuzzy segmentation of salient region of interest in low depth of field image
MMM'07: Proceedings of the 13th international conference on Multimedia Modeling - Volume Part I

Unsupervised segmenting region of interest in images is very useful in content-based application such as image indexing for content-based retrieval and target recognition. The proposed method applies fuzzy theory to separate the salient region of ...
Probabilistic consensus clustering using evidence accumulation

Clustering ensemble methods produce a consensus partition of a set of data points by combining the results of a collection of base clustering algorithms. In the evidence accumulation clustering (EAC) paradigm, the clustering ensemble is transformed into ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Pattern Recognition

Pattern Recognition Volume 46, Issue 10

October, 2013

236 pages

ISSN:0031-3203

Issue’s Table of Contents

Copyright © Elsevier Ltd © 2013.

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 01 October 2013

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

10
Total Citations
View Citations
0
Total Downloads

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

Reflects downloads up to 03 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Qin ZFu QPeng J(2024)A computationally efficient and robust looming perception model based on dynamic neural fieldNeural Networks10.1016/j.neunet.2024.106502179:COnline publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1016/j.neunet.2024.106502
Shan YLi SLi FCui YLi SChen MHe X(2024)Fuzzy self-consistent clustering ensembleApplied Soft Computing10.1016/j.asoc.2023.111151151:COnline publication date: 17-Apr-2024
https://dl.acm.org/doi/10.1016/j.asoc.2023.111151
Shi YYang KYu ZChen CZeng H(2023)Adaptive Ensemble Clustering With Boosting BLS-Based AutoencoderIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.327112035:12(12369-12383)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1109/TKDE.2023.3271120
Dai DYu ZHuang WHu YChen C(2023)Multi-Objective Cluster Ensemble based on Filter Refinement SchemeIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.320714135:8(8257-8269)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1109/TKDE.2022.3207141
Mahmoudi MAkbarzadeh HParvin HNejatian SRezaie VAlinejad-Rokny H(2021)Consensus function based on cluster-wise two level clusteringArtificial Intelligence Review10.1007/s10462-020-09862-154:1(639-665)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1007/s10462-020-09862-1
Pho KAkbarzadeh HParvin HNejatian SAlinejad-Rokny H(2021)A multi-level consensus function clustering ensembleSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-021-06092-725:21(13147-13165)Online publication date: 1-Nov-2021
https://dl.acm.org/doi/10.1007/s00500-021-06092-7
Dong XYu ZCao WShi YMa Q(2020)A survey on ensemble learningFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-019-8208-z14:2(241-258)Online publication date: 1-Apr-2020
https://dl.acm.org/doi/10.1007/s11704-019-8208-z
Abbasi SNejatian SParvin HRezaie VBagherifard K(2019)Clustering ensemble selection considering quality and diversityArtificial Intelligence Review10.1007/s10462-018-9642-252:2(1311-1340)Online publication date: 1-Aug-2019
https://dl.acm.org/doi/10.1007/s10462-018-9642-2
Song YZhang SHe BSha QShen YYan TNian RLendasse A(2018)Gaussian derivative models and ensemble extreme learning machine for texture image classificationNeurocomputing10.1016/j.neucom.2017.01.113277:C(53-64)Online publication date: 14-Feb-2018
https://dl.acm.org/doi/10.1016/j.neucom.2017.01.113
Memar SKsantini RBoufama B(2014)Multiple Object Detection with Occlusion Using Active Contour Model and Fuzzy C-MeanImage Analysis and Recognition10.1007/978-3-319-11758-4_25(224-233)Online publication date: 22-Oct-2014
https://dl.acm.org/doi/10.1007/978-3-319-11758-4_25

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents