Performance Evaluation of ROI Extraction Models from Stationary Images
In this paper three basic approaches and different
methods under each of them for extracting region of interest (ROI)
from stationary images are explored. The results obtained for each of
the proposed methods are shown, and it is demonstrated where each
method outperforms the other. Two main problems in ROI
extraction: the channel selection problem and the saliency reversal
problem are discussed and how best these two are addressed by
various methods is also seen. The basic approaches are 1) Saliency
based approach 2) Wavelet based approach 3) Clustering based
approach. The saliency approach performs well on images containing
objects of high saturation and brightness. The wavelet based
approach performs well on natural scene images that contain regions
of distinct textures. The mean shift clustering approach partitions the
image into regions according to the density distribution of pixel
intensities. The experimental results of various methodologies show
that each technique performs at different acceptable levels for
various types of images.
[1] L Itti, C Koch and E Niebur, " A Model of Saliency-Based Visual
Attention for Rapid Scene Analysis", Proc. IEEE Transactions on
Pattern Analysis and machine Intelligence, vol. 20,no. 11,November
1998.
[2] Xiaodi Hou, Liquing Zhang, "Saliency Detection: A Spectral Residual
Approach", Proc. IEEE Conference on Computer Vision and Pattern
recognition, June 2007.
[3] Zheshen Wang, Baoxin Li, "A Two Stage Approach to Saliency
Detection in Images", Proc. IEEE Conference on Acoustics, Speech and
Signal Processing, March 2008.
[4] Bin Zhang, Yafeng Zheng, Qiaorong Zhang, "Extracting Regions of
Interest Based on Phase Spectrum and Morphological Approach", Proc.
ISECS International Colloquium on Computing, Communication,
Control and Management, May 2009.
[5] Qiaorong Zhang, Huimin Xiao, "Extracting Regions of Interest in
Biomedical Images", Proc. International seminar on Future BioMedical
Information Engineering, December 2008.
[6] J.Harel, C.Koch, P.Perona, "Graph Based Visual Saliency", Proc. NIPS,
December 2006
[7] W.Xiangyang, Y.Hongying, H.Fengli,"A New Regions of Interest Based
Image Retrieval Using DWT", Proc. ISCIT, October 2005.
[8] Q.Zhou, L.Ma, M.Celenk and D.M. Chelberg, "Content Based Image
Retrieval Based on ROI Detection and Relevance Feedback",
Multimedia Tools Appl, 27(2), 2005.
[9] J.Goldberger, S.Gordon and H.Greenspan, "Unsupervised Image Set
Clustering Using an Information Theoretic Framework", IEEE Trans on
Systems, Man and Cybernetics, vol 37, no:5, October 2007
[10] Qiaorong Zhang, Yafeng Zheng, Yafeng Zheng, "Automatically
Extracting Salient Regions in Natural Images", Proc. ISECS
International Colloquium on Computing, Communication, Control and
Management, May 2009.
[11] R.C Gonzales, R.E Woods, Digital Image Processing, 2ndEdition,
Prentice Hall, ISBN 0-201-18075-8.
[1] L Itti, C Koch and E Niebur, " A Model of Saliency-Based Visual
Attention for Rapid Scene Analysis", Proc. IEEE Transactions on
Pattern Analysis and machine Intelligence, vol. 20,no. 11,November
1998.
[2] Xiaodi Hou, Liquing Zhang, "Saliency Detection: A Spectral Residual
Approach", Proc. IEEE Conference on Computer Vision and Pattern
recognition, June 2007.
[3] Zheshen Wang, Baoxin Li, "A Two Stage Approach to Saliency
Detection in Images", Proc. IEEE Conference on Acoustics, Speech and
Signal Processing, March 2008.
[4] Bin Zhang, Yafeng Zheng, Qiaorong Zhang, "Extracting Regions of
Interest Based on Phase Spectrum and Morphological Approach", Proc.
ISECS International Colloquium on Computing, Communication,
Control and Management, May 2009.
[5] Qiaorong Zhang, Huimin Xiao, "Extracting Regions of Interest in
Biomedical Images", Proc. International seminar on Future BioMedical
Information Engineering, December 2008.
[6] J.Harel, C.Koch, P.Perona, "Graph Based Visual Saliency", Proc. NIPS,
December 2006
[7] W.Xiangyang, Y.Hongying, H.Fengli,"A New Regions of Interest Based
Image Retrieval Using DWT", Proc. ISCIT, October 2005.
[8] Q.Zhou, L.Ma, M.Celenk and D.M. Chelberg, "Content Based Image
Retrieval Based on ROI Detection and Relevance Feedback",
Multimedia Tools Appl, 27(2), 2005.
[9] J.Goldberger, S.Gordon and H.Greenspan, "Unsupervised Image Set
Clustering Using an Information Theoretic Framework", IEEE Trans on
Systems, Man and Cybernetics, vol 37, no:5, October 2007
[10] Qiaorong Zhang, Yafeng Zheng, Yafeng Zheng, "Automatically
Extracting Salient Regions in Natural Images", Proc. ISECS
International Colloquium on Computing, Communication, Control and
Management, May 2009.
[11] R.C Gonzales, R.E Woods, Digital Image Processing, 2ndEdition,
Prentice Hall, ISBN 0-201-18075-8.
@article{"International Journal of Electrical, Electronic and Communication Sciences:62818", author = "K.V. Sridhar and Varun Gunnala and K.S.R Krishna Prasad", title = "Performance Evaluation of ROI Extraction Models from Stationary Images", abstract = "In this paper three basic approaches and different
methods under each of them for extracting region of interest (ROI)
from stationary images are explored. The results obtained for each of
the proposed methods are shown, and it is demonstrated where each
method outperforms the other. Two main problems in ROI
extraction: the channel selection problem and the saliency reversal
problem are discussed and how best these two are addressed by
various methods is also seen. The basic approaches are 1) Saliency
based approach 2) Wavelet based approach 3) Clustering based
approach. The saliency approach performs well on images containing
objects of high saturation and brightness. The wavelet based
approach performs well on natural scene images that contain regions
of distinct textures. The mean shift clustering approach partitions the
image into regions according to the density distribution of pixel
intensities. The experimental results of various methodologies show
that each technique performs at different acceptable levels for
various types of images.", keywords = "clustering, ROI, saliency, wavelets.", volume = "4", number = "12", pages = "1861-9", }