Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30835
Performance Evaluation of ROI Extraction Models from Stationary Images

Authors: K.V. Sridhar, Varun Gunnala, K.S.R Krishna Prasad


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, Wavelets, Saliency

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1103


[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.