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Online Access
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2015-12-30 12:05:56
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12580
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[1] L. G. Shapiro, I. Atmosukarto, H. Cho, H. J. Lin, S. Ruiz-Correa, and J. Yuen, “Similarity-Based Retrieval for Biomedical Applications”. Computational Intelligence Volume 73, 2008, pp 355-387. [2] D.S. Elizabeth, H.K. Nehemiah, C.S. Retmin Raj, A. Kannan,”Computer-aided diagnosis of lung cancer based on analysis of the significant slice of chest computed tomography image”, Image Processing, IET,vol. 6, no. 6, 2012, pp. 697-705. [3] S.Singh and D.V. Rao, “Recognition and identification of target images using feature based retrieval in UAV missions”, Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), 2013 Fourth National Conference, pp. 1-4, 18-21. [4] R. Zheng, S.Wen , Q.Zhang, H. Jin H and X.Xie,” Compounded Face Image Retrieval Based on Vertical Web Image Retrieval”, China grid Conference (ChinaGrid), 2011pp. 130-135, 22-23. [5] Z. Xinjuan, H. Junfang , and Z. Qianming, “Apparel image matting and applications in e-commerce”, Information Technology and Artificial Intelligence Conference (ITAIC), 6th IEEE Joint International Conference, 2011, vol. 2, pp. 278-282, 20-22. [6] V.S. Thakare, and N. Patil, “ Classification of texture using gray level co-occurrence matrix and self-organizing map”, Electronic Systems, Signal Processing and Computing Technologies (ICESC), International Conference, 2014,pp. 350-355, 9-11 [7] K.F.Hui ,” Image retrieval using both color and texture features”, Machine Learning and Cybernetics, International Conference, 2009,vol. 4, pp. 2228-2232, 12-15. [8] E. Rashedi , and P.H. Nezamabadi, “Improving the precision of CBIR systems by feature selection using binary gravitational search algorithm”, Artificial Intelligence and Signal Processing (AISP), 16th CSI International Symposium, 2012, pp. 39-42, 2-3. [9] B. Wang B, X. Zhang , Z.Y. Zhao , Z.D. Zhang , and H.X.Zhang, “A semantic description for content-based image retrieval ” , Computational Intelligence, 2008,Volume 73, 2008, pp 355-387. [10] Z.C. Huang, P.P.K Chan, W. W. Y.Ng, and D.S. Yeung, “Content-based image retrieval using color moment and Gabor texture feature”, Machine Learning and Cybernetics (ICMLC), International Conference, 2010, vol. 2, pp. 719-724, 11-14. [11] A. J. Afifi and W. M. Ashour. “Image Retrieval Based on Content Using Color Feature”. International Scholarly Research Network (ISRN) Computer Graphics, Volume 2012. [12] S.Kim, S.Park, and M.Kim, “Image Classification into Object / Non-object Classes”, CIVR 2004, LNCS 3115, 2004, pp.393-400, [13] A.H. Kam, Ng.T.T., N.G.Kingsbury, and W.J. Fitzgerald, “ Content Based Image Retrieval through Object Extraction and Querying”, IEEE Workshop on Content-based Access of Image and Video Libraries. (2000) 91-95 [14] D. Kinoshenko, V. Mashtalir, and E. Yegorova, “Clustering method for fast content-based image retrieval,” Computer Vision and Graphics, 32, Mar. 2006, pp. 946-952. [15] V. G. Tonge, “Content Based Image Retrieval by K-Means Clustering Algorithm,” International Journal of Engineering Science and Technology (IJEST), Feb 2011, pp. 46-49. [16] K. Sakthivel, T. Ravichandran, and C. Kavitha, “Performance enhancement in image retrieval using modified k-means clustering algorithm,” Journal of Mathematics and Technology, Feb 2010 pp. 78-85, Feb 2010. [17] Rajashekhara and S. Chaudhuri “Segmentation and region of interest based image retrieval in low depth of field observations”, Image and Vision Computing 25 (2007) 1709–1724. [18] R. Shettini, G. Ciocca and, S. Zuffi, “Color Imaging Science, “ Exploiting Digital Media”, John Wiley, 2001. [19] A.D. Bimbo, “Visual Information Retrieval”, Morgan Kaufmann.Publishers, Inc., San Francisco, USA, 2001. [20] M. Stricker and M. Orengo, “Similarity of colour images”, in: Proceedings of the SPIE Storage and Retrieval for Image and Video Databases III, Sanjose, CA, USA, 1995, vol. 2420, pp. 381–392. [21] S. Mangijao and K. Hemachandran, “Content-Based Image Retrieval using Color Moment and GaborTexture Feature”. IJCSI International Journal of Computer Science Issues, Sept. 2012, Vol. 9, Issue 5, No 1 [22] C.Carson, M.Thomas, S.Belongie, J.M. Hellerstein, and J.Malik, “Blobworld: A System for Region-Based Image Indexing and Retrieval”, VISUAL'99. Amsterdam, Netherlands, (1999) 509-516. [23] W.Wang, Y.Song, and Zhang, “ Semantics Retrieval by Region Saliency”, Int'l Conf. on Image and Video Retrieval, 2002, 29-37. [24] P.S. Suhasini,. K.S.R.Krishna, I. V. Murali Krishna,” Cbir Using Color Histogram Processing Journal of Theoretical and Applied Information Technology (Jatit). 2009. Vol.6.No.1 pp:116-122. [25] D.S. Zhang and G. Lu, “Generic Fourier descriptor for shape-basedimage retrieval”. Proceedings of IEEE International Conference on Multimedia and Expo (ICME2002), August 26–29, 2002,Vol. 1, Lausanne, Switzerland, , pp. 425–428. [26] D. S. Zambre, and S. P. Patil, “ Retrieving Content Based Images with Query point technique based on K-mean Clustering”, International Journal of Advancements in Research & Technology, Volume 2, Issue 4, April-2013. ISSN 2278-7763. [27] Y.Liu, D. Zhang, G.Lu, & W.Y.Ma. “A Survey of CBIR with High-level Semantics”. Pattern Recognition 40 (2007), pp 262-282.
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12580 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=12580 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Article Journal image/jpeg inches 96 96 norman 771 1415 43 43 2015-12-30 12:05:56 1415x771 6887-01-FH02-FIK-15-04678.jpg UniSZA Private Access Hybrid method to obtain interest region and non interest region for color based image retrieval International Journal of Advances in Soft Computing and its Applications Content based image retrieval (CBIR) has become one of the most active research areas in the past few years. Many indexing techniques are based on global feature distributions. However, these global distributions have limited discriminating power because they are unable to capture local image information. In this paper, the new proposed method based on local image to classify the Interest Region (IR) and Non Interest Region (NIR) of images. To develop this, the integration of clustering and user intervention was applied. Clustering process is obtaining several regions, meanwhile to ascertain the location of the center of images through user intervention. Several experiments are conducted using different weight (ω, γ) of IR and NIR. Subsequently average color moment is extracted from this region (IR and NIR) in CIE Lab color model. To investigate the performance, new distance is proposed based on Euclidean distance. Experimental results show the proposed method more efficient in image retrieval. 7 3 International Center for Scientific Research and Studies International Center for Scientific Research and Studies 16-30 [1] L. G. Shapiro, I. Atmosukarto, H. Cho, H. J. Lin, S. Ruiz-Correa, and J. Yuen, “Similarity-Based Retrieval for Biomedical Applications”. Computational Intelligence Volume 73, 2008, pp 355-387. [2] D.S. Elizabeth, H.K. Nehemiah, C.S. Retmin Raj, A. Kannan,”Computer-aided diagnosis of lung cancer based on analysis of the significant slice of chest computed tomography image”, Image Processing, IET,vol. 6, no. 6, 2012, pp. 697-705. [3] S.Singh and D.V. Rao, “Recognition and identification of target images using feature based retrieval in UAV missions”, Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), 2013 Fourth National Conference, pp. 1-4, 18-21. [4] R. Zheng, S.Wen , Q.Zhang, H. Jin H and X.Xie,” Compounded Face Image Retrieval Based on Vertical Web Image Retrieval”, China grid Conference (ChinaGrid), 2011pp. 130-135, 22-23. [5] Z. Xinjuan, H. Junfang , and Z. Qianming, “Apparel image matting and applications in e-commerce”, Information Technology and Artificial Intelligence Conference (ITAIC), 6th IEEE Joint International Conference, 2011, vol. 2, pp. 278-282, 20-22. [6] V.S. Thakare, and N. Patil, “ Classification of texture using gray level co-occurrence matrix and self-organizing map”, Electronic Systems, Signal Processing and Computing Technologies (ICESC), International Conference, 2014,pp. 350-355, 9-11 [7] K.F.Hui ,” Image retrieval using both color and texture features”, Machine Learning and Cybernetics, International Conference, 2009,vol. 4, pp. 2228-2232, 12-15. [8] E. Rashedi , and P.H. Nezamabadi, “Improving the precision of CBIR systems by feature selection using binary gravitational search algorithm”, Artificial Intelligence and Signal Processing (AISP), 16th CSI International Symposium, 2012, pp. 39-42, 2-3. [9] B. Wang B, X. Zhang , Z.Y. Zhao , Z.D. Zhang , and H.X.Zhang, “A semantic description for content-based image retrieval ” , Computational Intelligence, 2008,Volume 73, 2008, pp 355-387. [10] Z.C. Huang, P.P.K Chan, W. W. Y.Ng, and D.S. Yeung, “Content-based image retrieval using color moment and Gabor texture feature”, Machine Learning and Cybernetics (ICMLC), International Conference, 2010, vol. 2, pp. 719-724, 11-14. [11] A. J. Afifi and W. M. Ashour. “Image Retrieval Based on Content Using Color Feature”. International Scholarly Research Network (ISRN) Computer Graphics, Volume 2012. [12] S.Kim, S.Park, and M.Kim, “Image Classification into Object / Non-object Classes”, CIVR 2004, LNCS 3115, 2004, pp.393-400, [13] A.H. Kam, Ng.T.T., N.G.Kingsbury, and W.J. Fitzgerald, “ Content Based Image Retrieval through Object Extraction and Querying”, IEEE Workshop on Content-based Access of Image and Video Libraries. (2000) 91-95 [14] D. Kinoshenko, V. Mashtalir, and E. Yegorova, “Clustering method for fast content-based image retrieval,” Computer Vision and Graphics, 32, Mar. 2006, pp. 946-952. [15] V. G. Tonge, “Content Based Image Retrieval by K-Means Clustering Algorithm,” International Journal of Engineering Science and Technology (IJEST), Feb 2011, pp. 46-49. [16] K. Sakthivel, T. Ravichandran, and C. Kavitha, “Performance enhancement in image retrieval using modified k-means clustering algorithm,” Journal of Mathematics and Technology, Feb 2010 pp. 78-85, Feb 2010. [17] Rajashekhara and S. Chaudhuri “Segmentation and region of interest based image retrieval in low depth of field observations”, Image and Vision Computing 25 (2007) 1709–1724. [18] R. Shettini, G. Ciocca and, S. Zuffi, “Color Imaging Science, “ Exploiting Digital Media”, John Wiley, 2001. [19] A.D. Bimbo, “Visual Information Retrieval”, Morgan Kaufmann.Publishers, Inc., San Francisco, USA, 2001. [20] M. Stricker and M. Orengo, “Similarity of colour images”, in: Proceedings of the SPIE Storage and Retrieval for Image and Video Databases III, Sanjose, CA, USA, 1995, vol. 2420, pp. 381–392. [21] S. Mangijao and K. Hemachandran, “Content-Based Image Retrieval using Color Moment and GaborTexture Feature”. IJCSI International Journal of Computer Science Issues, Sept. 2012, Vol. 9, Issue 5, No 1 [22] C.Carson, M.Thomas, S.Belongie, J.M. Hellerstein, and J.Malik, “Blobworld: A System for Region-Based Image Indexing and Retrieval”, VISUAL'99. Amsterdam, Netherlands, (1999) 509-516. [23] W.Wang, Y.Song, and Zhang, “ Semantics Retrieval by Region Saliency”, Int'l Conf. on Image and Video Retrieval, 2002, 29-37. [24] P.S. Suhasini,. K.S.R.Krishna, I. V. Murali Krishna,” Cbir Using Color Histogram Processing Journal of Theoretical and Applied Information Technology (Jatit). 2009. Vol.6.No.1 pp:116-122. [25] D.S. Zhang and G. Lu, “Generic Fourier descriptor for shape-basedimage retrieval”. Proceedings of IEEE International Conference on Multimedia and Expo (ICME2002), August 26–29, 2002,Vol. 1, Lausanne, Switzerland, , pp. 425–428. [26] D. S. Zambre, and S. P. Patil, “ Retrieving Content Based Images with Query point technique based on K-mean Clustering”, International Journal of Advancements in Research & Technology, Volume 2, Issue 4, April-2013. ISSN 2278-7763. [27] Y.Liu, D. Zhang, G.Lu, & W.Y.Ma. “A Survey of CBIR with High-level Semantics”. Pattern Recognition 40 (2007), pp 262-282.
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| spellingShingle |
Hybrid method to obtain interest region and non interest region for color based image retrieval
|
| summary |
Content based image retrieval (CBIR) has become one of the most active research areas in the past few years. Many indexing techniques are based on global feature distributions. However, these global distributions have limited discriminating power because they are unable to capture local image information. In this paper, the new proposed method based on local image to classify the Interest Region (IR) and Non Interest Region (NIR) of images. To develop this, the integration of clustering and user intervention was applied. Clustering process is obtaining several regions, meanwhile to ascertain the location of the center of images through user intervention. Several experiments are conducted using different weight (ω, γ) of IR and NIR. Subsequently average color moment is extracted from this region (IR and NIR) in CIE Lab color model. To investigate the performance, new distance is proposed based on Euclidean distance. Experimental results show the proposed method more efficient in image retrieval.
|
| title |
Hybrid method to obtain interest region and non interest region for color based image retrieval
|
| title_full |
Hybrid method to obtain interest region and non interest region for color based image retrieval
|
| title_fullStr |
Hybrid method to obtain interest region and non interest region for color based image retrieval
|
| title_full_unstemmed |
Hybrid method to obtain interest region and non interest region for color based image retrieval
|
| title_short |
Hybrid method to obtain interest region and non interest region for color based image retrieval
|
| title_sort |
hybrid method to obtain interest region and non interest region for color based image retrieval
|