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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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2024-08-26 23:31:45
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Restricted Document
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12381
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UniSZA
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[1] Ryszard, Image Feature Extraction Techniques and Their Applications for CBIR and Biometrics Systems. International Journal of Biology and Biomedical Engineering, 2007. Vol 1(Issue 1). [2] Hedberg, H., A Survey of Various Image Segmentation Techniques. [3] Khan, M.W., A Survey: Image Segmentation Techniques. International Journal of Future Computer and Communication, 2014. Vol 3. [4] V. Dey a, Y. Zhanga , M. Zhongb , A Review On Image Segmentation Techniques With Remote Sensing Perspective. (ISPRS10), 2010. VOL. XXXVIII(Part 7A). [5] OTSU, N., A Threshold Selection Method from Gray-Level Histograms. 2EEE TRANSACTIONS ON SYSTREMS, MAN, AND CYBERNETICS, 1979. Vol 9. [6] Ali Abdo Mohammed Al-Kubati, J.A.M.S., Murad A. A. Taher. Evaluation of Canny and Otsu Image Segmentation. in International Conference on Emerging Trends in Computer and Electronics Engineering (ICETCEE'2012). 2012. Dubai. [7] Shahzad*, A., et al., Enhanced Watershed Image Processing Segmentation. Journal of Information & Communication Technology, 2008. Vol 2. [8] Soille, L.V.a.P., Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations, in IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE. 1991. [9] Qinghua Ji, R.S. A noval method of image segmentation using watershed transformation. in International Conference on Computer Science and Network Technology. 2011: IEEE. [10] Pandey, S., STUDY AND IMPLEMENTATION OF MORPHOLOGY FOR IMAGE SEGMENTATION, in DEPARTMENT OF ELECTRICAL AND INSTRUMENTATION ENGINEERING. 2010, THAPAR UNIVERSITY. p. 76. [11] Yubin2, L.Y.L. An Algorithm of Image Segmentation Based on Fuzzy Mathematical Morphology. in International Forum on Information Technology and Applications. 2009. [12] ; Available from: http://www.mathworks.com/help/images/ref/bwlabel.html. [13] Joao Ferreira Nunes, P.M.M., Joao Manuel R.S. Travares, Shape Based Image Retrieval and Classification. 2010. [14] Oncel Tuzel1, Fatih Porikli3 , and Peter Meer1,2. Region Covariance: A Fast Descriptor for Detection and Classification. in European Conference on Computer Vision (ECCV). 2006. 201 Broadway, Cambridge, Massachusetts. [15] Mokhairi Makhtar, D.C.N., Mick Ridley, Predictive Model Representation and Comparison: Towards Data and Predictive Models Governance. 2010. [16] Weng, D.L.Q., A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 2007. [17] L. Cinquea, G. Forestib , L. Lombardic , A clustering fuzzy approach for image segmentation. Pattern Recognition, 2003. [18] Manjusha Singh1, A.M., A Survey Paper on Various Visual Image Segmentation Techniques. International Journal of Computer Science and Management Research, 2013. Vol 2(Issue 1): p. 7. [19] Miss Hetal J. Vala, P.A.B., A Review on Otsu Image Segmentation Algorithm. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 2013. Volume 2(Issue 2). [20] P. K. SAHOO, S.S., AND A. K. C. WONG, A SURVEY OF THRESHOLDING TECHNIQUES, in COMPUTER VISION, GRAPHICS, AND IMAGE PROCESSING 1988. p. 233-260. [21] Mengxing Huang, W.Y., Donghai Zhu, An Improved Image Segmentation Algorithm Based on the Otsu Method, in ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing. 2012. [22] Information Retrieval. 2 June 2013 [cited 2012; Available from: http://en.wikipedia.org/wiki/Information_retrieval. [23] Gabbouj, E.G.M., Feature selection for content-based image retrieval. 2008.
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12381 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=12381 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Article Journal application/pdf Adobe Acrobat Pro DC 20 Paper Capture Plug-in with ClearScan 13 1.6 Adobe Acrobat Pro DC 20.6.20042 2024-08-26 23:31:45 6681-01-FH02-FIK-15-03852.pdf UniSZA Private Access The Contribution of Feature Selection and Morphological Operation For On-Line Business System’s Image Classification International Journal of Multimedia and Ubiquitous Engineering Automatic image annotation is one of crucial and attractive field of image retrieval. Classification process is part of the important phase in automatic image annotation (AIA). With the explosive growth of methods in this research area, this paper proposes 5 processing steps before image annotation using Amazon dataset, i.e., image segmentation, object identification, feature extraction, feature selection and image features classification. A lot of research has been done in creating numbers of different approaches and algorithm for image segmentation. Otsu is one of the most well known method in image segmentation region based. The proposed model aims to provide the highest accuracy after undergo those processing steps. This paper conducted several experiments for image classification starting from image segmentation in order to demonstrate usefulness and competiveness among different type of classifiers. It also target to study the effect of morphological operation and feature selection to the accuracy. For the classification experiment, it was tested using four types of classifiers: BayesNet, NaiveBayesUpdateable, RandomTree and IBk. 10 11 Science and Engineering Research Support Society Science and Engineering Research Support Society 303-314 [1] Ryszard, Image Feature Extraction Techniques and Their Applications for CBIR and Biometrics Systems. International Journal of Biology and Biomedical Engineering, 2007. Vol 1(Issue 1). [2] Hedberg, H., A Survey of Various Image Segmentation Techniques. [3] Khan, M.W., A Survey: Image Segmentation Techniques. International Journal of Future Computer and Communication, 2014. Vol 3. [4] V. Dey a, Y. Zhanga , M. Zhongb , A Review On Image Segmentation Techniques With Remote Sensing Perspective. (ISPRS10), 2010. VOL. XXXVIII(Part 7A). [5] OTSU, N., A Threshold Selection Method from Gray-Level Histograms. 2EEE TRANSACTIONS ON SYSTREMS, MAN, AND CYBERNETICS, 1979. Vol 9. [6] Ali Abdo Mohammed Al-Kubati, J.A.M.S., Murad A. A. Taher. Evaluation of Canny and Otsu Image Segmentation. in International Conference on Emerging Trends in Computer and Electronics Engineering (ICETCEE'2012). 2012. Dubai. [7] Shahzad*, A., et al., Enhanced Watershed Image Processing Segmentation. Journal of Information & Communication Technology, 2008. Vol 2. [8] Soille, L.V.a.P., Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations, in IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE. 1991. [9] Qinghua Ji, R.S. A noval method of image segmentation using watershed transformation. in International Conference on Computer Science and Network Technology. 2011: IEEE. [10] Pandey, S., STUDY AND IMPLEMENTATION OF MORPHOLOGY FOR IMAGE SEGMENTATION, in DEPARTMENT OF ELECTRICAL AND INSTRUMENTATION ENGINEERING. 2010, THAPAR UNIVERSITY. p. 76. [11] Yubin2, L.Y.L. An Algorithm of Image Segmentation Based on Fuzzy Mathematical Morphology. in International Forum on Information Technology and Applications. 2009. [12] ; Available from: http://www.mathworks.com/help/images/ref/bwlabel.html. [13] Joao Ferreira Nunes, P.M.M., Joao Manuel R.S. Travares, Shape Based Image Retrieval and Classification. 2010. [14] Oncel Tuzel1, Fatih Porikli3 , and Peter Meer1,2. Region Covariance: A Fast Descriptor for Detection and Classification. in European Conference on Computer Vision (ECCV). 2006. 201 Broadway, Cambridge, Massachusetts. [15] Mokhairi Makhtar, D.C.N., Mick Ridley, Predictive Model Representation and Comparison: Towards Data and Predictive Models Governance. 2010. [16] Weng, D.L.Q., A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 2007. [17] L. Cinquea, G. Forestib , L. Lombardic , A clustering fuzzy approach for image segmentation. Pattern Recognition, 2003. [18] Manjusha Singh1, A.M., A Survey Paper on Various Visual Image Segmentation Techniques. International Journal of Computer Science and Management Research, 2013. Vol 2(Issue 1): p. 7. [19] Miss Hetal J. Vala, P.A.B., A Review on Otsu Image Segmentation Algorithm. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 2013. Volume 2(Issue 2). [20] P. K. SAHOO, S.S., AND A. K. C. WONG, A SURVEY OF THRESHOLDING TECHNIQUES, in COMPUTER VISION, GRAPHICS, AND IMAGE PROCESSING 1988. p. 233-260. [21] Mengxing Huang, W.Y., Donghai Zhu, An Improved Image Segmentation Algorithm Based on the Otsu Method, in ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing. 2012. [22] Information Retrieval. 2 June 2013 [cited 2012; Available from: http://en.wikipedia.org/wiki/Information_retrieval. [23] Gabbouj, E.G.M., Feature selection for content-based image retrieval. 2008.
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| spellingShingle |
The Contribution of Feature Selection and Morphological Operation For On-Line Business System’s Image Classification
|
| summary |
Automatic image annotation is one of crucial and attractive field of image retrieval. Classification process is part of the important phase in automatic image annotation (AIA). With the explosive growth of methods in this research area, this paper proposes 5 processing steps before image annotation using Amazon dataset, i.e., image segmentation, object identification, feature extraction, feature selection and image features classification. A lot of research has been done in creating numbers of different approaches and algorithm for image segmentation. Otsu is one of the most well known method in image segmentation region based. The proposed model aims to provide the highest accuracy after undergo those processing steps. This paper conducted several experiments for image classification starting from image segmentation in order to demonstrate usefulness and competiveness among different type of classifiers. It also target to study the effect of morphological operation and feature selection to the accuracy. For the classification experiment, it was tested using four types of classifiers: BayesNet, NaiveBayesUpdateable, RandomTree and IBk.
|
| title |
The Contribution of Feature Selection and Morphological Operation For On-Line Business System’s Image Classification
|
| title_full |
The Contribution of Feature Selection and Morphological Operation For On-Line Business System’s Image Classification
|
| title_fullStr |
The Contribution of Feature Selection and Morphological Operation For On-Line Business System’s Image Classification
|
| title_full_unstemmed |
The Contribution of Feature Selection and Morphological Operation For On-Line Business System’s Image Classification
|
| title_short |
The Contribution of Feature Selection and Morphological Operation For On-Line Business System’s Image Classification
|
| title_sort |
contribution of feature selection and morphological operation for on-line business system’s image classification
|