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INTELEK Repository
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Online Access
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https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072
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2016-03-15 13:18:28
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12882
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[1] V. S. Sural and A. K. Majundar, “An Integrated and Intensity Co-occurrence Matrix,” Pattern Recognition Letters, vol. 28, (2007), pp. 974-983. [2] M. J. Swain and D. H. Ballard, “Color indexing”, Internat. J. Comput, vol. 7, (1991), pp. 11–32. [3] M. Haralick, “Texture Image Classification”, IEEE Transactions On Systems, Man and Cybenertics, vol. 3, no. 6, (1973), pp. 610-621. [4] A. Porebski, N. Vandenbroucke and L. Macaire, “Neighborhood and Haralick feature extraction for color texture analysis”, In Proceeding of the 4th European Conference on Colour in Graphics, Image and Vision (CGIV'08), Terrassa, Spain, (2008), pp. 316-321. [5] X. Li, S. C. Chen, M. L. Shyu and B. Furth, “An Effective Content-based Visual Image Retrieval System”, Computer Software and Application Conference (COMPSAC), Oxford, England, (2002), pp. 914-919. [6] V. Arvis, C. Debain, M. Berducat and A. Benassi, “Generalization of the Cooccurence Matrix For Colour Image”, Application to Colour Texture Classification. Image Analysis and Streology, vol. 23, (2004), pp. 63-72. [7] A. Drimbarean and P. F. Whelan, “Experiments in Color Texture Analysis”, Pattern Recognition Letter, vol. 22, (2001), pp. 7-1161. [8] Y. Liu, D. Zhang, G. Lu and W. Y. Ma, “A Survey of CBIR with High-level Semantics”, Pattern Recognition, vol. 40, (2007), pp. 262-282. [9] A. Moulay, X. Maldague and W. B. Larbi, “A New Color-Texture Approach for Industrial Products Inspection”, Journal Of Multimedia, no. 3, (2008). [10] J. Li and J. Z. Wang, “Automatic linguistic indexing of pictures by a statistical modeling approach”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 9, (2003), pp. 1075-1088. [11] C. Palm, “Color Texture Classification by integrative co-occurrence matrices”, Pattern Recognition, vol. 37, (2004), pp. 965-975. [12] M. B. Rao, B. P. Rao and A. Govardhan, “CTDCIRS: Content based Image Retrieval System based on Dominant Color and Texture Features”, International Journal of Computer Applications. vol. 18, no. 6, (2011). [13] T. Bhattacharjee, B. Banerjee and N. Chowdhury, “An Interactive Content Based Image Retrieval Technique and Evaluation of its Performance in High Dimensional and Low Dimensional Space”, International Journal of Image Processing (IJIP), vol. 4, no. 4, (2010), pp. 329. [14] H. Shahbazi, M. Soryani and P. Kabiri, “Content Based Multispectral Image Retrieval Using Principal Component Analysis”, CIVR, (2008). [15] D. F. Morrison, “Multivariate statistical methods”, New York: McGraw-Hill, (1967). [16] S. Mangijao and K. Hemachandran, “Content-Based Image Retrieval using Color Moment and GaborTexture Feature”, IJCSI International Journal of Computer Science Issues, vol. 9, Issue 5, no. 1, (2012). [17] S. I. Lidsay, “A tutorial on Principal Components Analysis”, University of Otago, New Zealand, (2002). [18] O. Rourke, N. L. Hatcher and E. J. Stepanski, “A step-by-step approach to using SAS for univariate and multivariate statistics”, Second Edition. SAS Institute, Inc., Cary, North Carolina, USA, (2005). [19] R. B. Cattell, “Handbook of multivariate experimental psychology”, Chicago: Rand McNally, (1966). [20] A. R. Mamat, M. Muhammad, M. I. Awang, N. A. Rawi, M. F. A. Kadir and M. K. Awang, “Selecting Haralick‟s Texture Features on Color Co-occurrence Matrix for Image Retrieval using Average Analysis for Image Retrieval”, The Third International Conference on Informatics & Applications (ICIA2014), (2014). [21] S. Simone and R. Jain, “Similarity Measures”, IEEE Transactions On Pattern Analysis And Machine Intelligence, vol. 21, no. 9, (1999). [22] G. Qian, S. Sural and G. S. Pramanik, “Similarity between Euclidean and cosine angle distance fornearest neighbor queries”, In Proceedings of the 2004 ACM symposium on Applied computing, ACM, (2004), pp 1232-1237. [23] D. Zhang, and G. Lu, “Evaluation of similarity measurement for image retrieval”, In Neural Networks and Signal Processing. Proceedings of the 2003 International Conference on, IEEE, vol. 2, (2003), pp. 928-931.
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12882 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=12882 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Article Journal image/jpeg inches 96 96 norman 1411 44 44 736 2016-03-15 13:18:28 1411x736 7189-01-FH02-FIK-16-05471.jpg UniSZA Private Access Average analysis method in selecting haralick’s texture features on color co-occurrence matrix for texture based image retrieval International Journal of Multimedia and Ubiquitous Engineering Many textures based image retrieval researchers use global texture features for representing and retrieval of images from an image database. However, this leads to misrepresentation of local information leading to the inefficient image retrieval performance. This paper presents an approach to overcome the problem. The approach focuses on extracting local Haralick’s texture feature based on a predetermined region using the color co-occurrence matrix method, the selection of the ‘significant’ Haralik’s texture features and evaluation of the performance of the combination of the ‘significant’ features. The proposed method which is an Average Analysis and a well known method, Principal Component Analysis were applied to obtain ‘significant’ features. In order to compare the performance, a series of experiments were carried out for both methods, which is the proposed Average Analysis and the Principal Component Analysis. Experiments were performed on a 1000 selected images from the Coral image database which were divided into ten categories. Based on the experimental results, it is interesting to note that for the combination ‘significant’ features obtained from the proposed Average Analysis showed better retrieval performance compared to the Principal Component Analysis for almost all categories. This finding has an important implication in deciding the correct combination of ‘significant’ features for certain image properties. It has shown that the proposed method is able to produce less computational processing time due to a reduced amount of processing involved. The result is also compared to the previous researches and has shown an increase of an average precision from 8.5% to 26%. 11 2 Science and Engineering Research Support Society Science and Engineering Research Support Society 79-88 [1] V. S. Sural and A. K. Majundar, “An Integrated and Intensity Co-occurrence Matrix,” Pattern Recognition Letters, vol. 28, (2007), pp. 974-983. [2] M. J. Swain and D. H. Ballard, “Color indexing”, Internat. J. Comput, vol. 7, (1991), pp. 11–32. [3] M. Haralick, “Texture Image Classification”, IEEE Transactions On Systems, Man and Cybenertics, vol. 3, no. 6, (1973), pp. 610-621. [4] A. Porebski, N. Vandenbroucke and L. Macaire, “Neighborhood and Haralick feature extraction for color texture analysis”, In Proceeding of the 4th European Conference on Colour in Graphics, Image and Vision (CGIV'08), Terrassa, Spain, (2008), pp. 316-321. [5] X. Li, S. C. Chen, M. L. Shyu and B. Furth, “An Effective Content-based Visual Image Retrieval System”, Computer Software and Application Conference (COMPSAC), Oxford, England, (2002), pp. 914-919. [6] V. Arvis, C. Debain, M. Berducat and A. Benassi, “Generalization of the Cooccurence Matrix For Colour Image”, Application to Colour Texture Classification. Image Analysis and Streology, vol. 23, (2004), pp. 63-72. [7] A. Drimbarean and P. F. Whelan, “Experiments in Color Texture Analysis”, Pattern Recognition Letter, vol. 22, (2001), pp. 7-1161. [8] Y. Liu, D. Zhang, G. Lu and W. Y. Ma, “A Survey of CBIR with High-level Semantics”, Pattern Recognition, vol. 40, (2007), pp. 262-282. [9] A. Moulay, X. Maldague and W. B. Larbi, “A New Color-Texture Approach for Industrial Products Inspection”, Journal Of Multimedia, no. 3, (2008). [10] J. Li and J. Z. Wang, “Automatic linguistic indexing of pictures by a statistical modeling approach”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 9, (2003), pp. 1075-1088. [11] C. Palm, “Color Texture Classification by integrative co-occurrence matrices”, Pattern Recognition, vol. 37, (2004), pp. 965-975. [12] M. B. Rao, B. P. Rao and A. Govardhan, “CTDCIRS: Content based Image Retrieval System based on Dominant Color and Texture Features”, International Journal of Computer Applications. vol. 18, no. 6, (2011). [13] T. Bhattacharjee, B. Banerjee and N. Chowdhury, “An Interactive Content Based Image Retrieval Technique and Evaluation of its Performance in High Dimensional and Low Dimensional Space”, International Journal of Image Processing (IJIP), vol. 4, no. 4, (2010), pp. 329. [14] H. Shahbazi, M. Soryani and P. Kabiri, “Content Based Multispectral Image Retrieval Using Principal Component Analysis”, CIVR, (2008). [15] D. F. Morrison, “Multivariate statistical methods”, New York: McGraw-Hill, (1967). [16] S. Mangijao and K. Hemachandran, “Content-Based Image Retrieval using Color Moment and GaborTexture Feature”, IJCSI International Journal of Computer Science Issues, vol. 9, Issue 5, no. 1, (2012). [17] S. I. Lidsay, “A tutorial on Principal Components Analysis”, University of Otago, New Zealand, (2002). [18] O. Rourke, N. L. Hatcher and E. J. Stepanski, “A step-by-step approach to using SAS for univariate and multivariate statistics”, Second Edition. SAS Institute, Inc., Cary, North Carolina, USA, (2005). [19] R. B. Cattell, “Handbook of multivariate experimental psychology”, Chicago: Rand McNally, (1966). [20] A. R. Mamat, M. Muhammad, M. I. Awang, N. A. Rawi, M. F. A. Kadir and M. K. Awang, “Selecting Haralick‟s Texture Features on Color Co-occurrence Matrix for Image Retrieval using Average Analysis for Image Retrieval”, The Third International Conference on Informatics & Applications (ICIA2014), (2014). [21] S. Simone and R. Jain, “Similarity Measures”, IEEE Transactions On Pattern Analysis And Machine Intelligence, vol. 21, no. 9, (1999). [22] G. Qian, S. Sural and G. S. Pramanik, “Similarity between Euclidean and cosine angle distance fornearest neighbor queries”, In Proceedings of the 2004 ACM symposium on Applied computing, ACM, (2004), pp 1232-1237. [23] D. Zhang, and G. Lu, “Evaluation of similarity measurement for image retrieval”, In Neural Networks and Signal Processing. Proceedings of the 2003 International Conference on, IEEE, vol. 2, (2003), pp. 928-931.
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| spellingShingle |
Average analysis method in selecting haralick’s texture features on color co-occurrence matrix for texture based image retrieval
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| summary |
Many textures based image retrieval researchers use global texture features for representing and retrieval of images from an image database. However, this leads to misrepresentation of local information leading to the inefficient image retrieval performance. This paper presents an approach to overcome the problem. The approach focuses on extracting local Haralick’s texture feature based on a predetermined region using the color co-occurrence matrix method, the selection of the ‘significant’ Haralik’s texture features and evaluation of the performance of the combination of the ‘significant’ features. The proposed method which is an Average Analysis and a well known method, Principal Component Analysis were applied to obtain ‘significant’ features. In order to compare the performance, a series of experiments were carried out for both methods, which is the proposed Average Analysis and the Principal Component Analysis. Experiments were performed on a 1000 selected images from the Coral image database which were divided into ten categories. Based on the experimental results, it is interesting to note that for the combination ‘significant’ features obtained from the proposed Average Analysis showed better retrieval performance compared to the Principal Component Analysis for almost all categories. This finding has an important implication in deciding the correct combination of ‘significant’ features for certain image properties. It has shown that the proposed method is able to produce less computational processing time due to a reduced amount of processing involved. The result is also compared to the previous researches and has shown an increase of an average precision from 8.5% to 26%.
|
| title |
Average analysis method in selecting haralick’s texture features on color co-occurrence matrix for texture based image retrieval
|
| title_full |
Average analysis method in selecting haralick’s texture features on color co-occurrence matrix for texture based image retrieval
|
| title_fullStr |
Average analysis method in selecting haralick’s texture features on color co-occurrence matrix for texture based image retrieval
|
| title_full_unstemmed |
Average analysis method in selecting haralick’s texture features on color co-occurrence matrix for texture based image retrieval
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| title_short |
Average analysis method in selecting haralick’s texture features on color co-occurrence matrix for texture based image retrieval
|
| title_sort |
average analysis method in selecting haralick’s texture features on color co-occurrence matrix for texture based image retrieval
|