Comparison Of Image Classification Techniques Using Caltech 101 Dataset
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| internalnotes | [1] P. Kamavisdar, S. Saluja, and S. Agrawal, “A Survey on Image Classification Approaches and Techniques,” vol. 2, no. 1, pp. 1005–1009, 2013. [2] D. L. & Q. Weng, “A survey of image classification methods and techniques for improving classification performance,” Int. J. Remote Sens., 2007. [3] Mokhairi Makhtar, Engku Fadzli, and Shazwani Kamarudin, “The Contribution of Feature Selection and Morphological Operation For On- Line Business System ’ s Image Classification,” World Appl. Sci. J., 2014. [4] J. F. Nunes, P. M. Moreira, and J. M. R. S. Tavares, “Shape Based Image Retrieval and Classification,” 2010, pp. 433–438. [5] Fei-Fei, Fergus, and Perona, “Caltech 101 dataset,” 2004. [Online]. Available: http://www.vision.caltech.edu/feifeili/Datase ts.htm. [6] A. C. Berg, T. L. Berg, J. Malik, and U. C. Berkeley, “Shape Matching and Object Recognition using Low Distortion Correspondences,” 2005. [7] L. Fei-Fei, R. Fergus, and P. Perona, “Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories,” Conf. Comput. Vis. Pattern Recognit. Work., pp. 178–178, 2004. [8] S. Banerji, A. Sinha, and C. Liu, “New image descriptors based on color, texture, shape, and wavelets for object and scene image classification,” Neurocomputing, vol. 117, pp. 173–185, Oct. 2013. [9] P. A. B. Miss Hetal J. Vala, “A Review on Otsu Image Segmentation Algorithm,” Int. J. Adv. Res. Comput. Eng. Technol., vol. Volume 2, no. Issue 2, 2013. [10] R. S. Chora, “Image Feature Extraction Techniques and Their Applications for CBIR and Biometrics Systems,” Int. J. Biol. Biomed. Eng., vol. 1, no. 1, 2007. [11] M. M. Dengsheng Zhang n GuojunLu, “A review on automatic image annotation techniques,” Pattern Recognit., pp. 346–362, 2012. [12] P. . Sahoo, S. Soltani, and a. K. . Wong, “A survey of thresholding techniques,” Comput. Vision, Graph. Image Process., vol. 41, no. 2, pp. 233–260, Feb. 1988. [13] N. OTSU, “A Threshold Selection Method from Gray-Level Histograms,” 2EEE Trans. SYSTREMS, MAN, Cybern., vol. Vol 9, 1979. [14] A. A. M. Al-kubati, J. A. M. Saif, and M. A. A. Taher, “Evaluation of Canny and Otsu Image Segmentation,” pp. 24–26, 2012. [15] M. Huang, W. Yu, and D. Zhu, “An Improved Image Segmentation Algorithm Based on the Otsu Method,” 2012 13th ACIS Int. Conf. Softw. Eng. Artif. Intell. Netw. Parallel/Distributed Comput., pp. 135–139, Aug. 2012. [16] Ruggeri F., Faltin F., and Kenett R., “Bayesian Networks,” Encyclopedia of Statistics in Quality & Reliability. Wiley & Sons, 2007. [17] P.Santhi and V. Murali Bhaskaran, “Improving the Performance of Data Mining Algorithms in Health Care Data,” IJCST, vol. 2, no. 3, pp. 152–157, 2011. [18] I. Guyon, “An Introduction to Variable and Feature Selection,” vol. 3, pp. 1157–1182, 2003. |
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| spelling | 11501 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=11501 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Article Journal UniSZA Unisza unisza image/jpeg inches 758 96 96 1419 24 24 1419x758 2015-01-25 12:24:34 5748-01-FH02-FIK-15-02472.jpg UniSZA Private Access Comparison Of Image Classification Techniques Using Caltech 101 Dataset Journal of Theoretical and Applied Information Technology This paper presents the technique for the classification of single object images. First, this paper aims to introduce the efficient technique in order to classify single object image. Second, each single methods uses in order to propose the techniques were elaborated in this paper. It start from image segmentation, object identification, feature extraction, feature selection and classification. Finally, the best classifier that can provide the best results were identified. The efficiency of the proposed method is define by comparing the result of classification using two different datasets from author’s previous paper. The obligation for development of image classification has been improved due to remarkable growth in volume of images, as well as the widespread application in multiple fields. This paper explores the process of classifying images by the categories of object in the case of a large number of object categories. The use of a set of features to describe 2D shapes in low-level images has been proposed. The proposed technique aims a short and simple way to extract shape description before classifying the image. Using the Caltech 101 object recognition benchmark, classification was tested using four different classifiers; BayesNet, NaiveBayesUpdateable, Random Tree and IBk. Estimated accuracy was in the range from 58% to 99% (using 10-cross validation). By comparing with Amazon data, it is proved that the proposed model is more suitable for single object image. Amazon images give higher accuracy with the range from 80% to 99.48%. 71 1 79-86 [1] P. Kamavisdar, S. Saluja, and S. Agrawal, “A Survey on Image Classification Approaches and Techniques,” vol. 2, no. 1, pp. 1005–1009, 2013. [2] D. L. & Q. Weng, “A survey of image classification methods and techniques for improving classification performance,” Int. J. Remote Sens., 2007. [3] Mokhairi Makhtar, Engku Fadzli, and Shazwani Kamarudin, “The Contribution of Feature Selection and Morphological Operation For On- Line Business System ’ s Image Classification,” World Appl. Sci. J., 2014. [4] J. F. Nunes, P. M. Moreira, and J. M. R. S. Tavares, “Shape Based Image Retrieval and Classification,” 2010, pp. 433–438. [5] Fei-Fei, Fergus, and Perona, “Caltech 101 dataset,” 2004. [Online]. Available: http://www.vision.caltech.edu/feifeili/Datase ts.htm. [6] A. C. Berg, T. L. Berg, J. Malik, and U. C. Berkeley, “Shape Matching and Object Recognition using Low Distortion Correspondences,” 2005. [7] L. Fei-Fei, R. Fergus, and P. Perona, “Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories,” Conf. Comput. Vis. Pattern Recognit. Work., pp. 178–178, 2004. [8] S. Banerji, A. Sinha, and C. Liu, “New image descriptors based on color, texture, shape, and wavelets for object and scene image classification,” Neurocomputing, vol. 117, pp. 173–185, Oct. 2013. [9] P. A. B. Miss Hetal J. Vala, “A Review on Otsu Image Segmentation Algorithm,” Int. J. Adv. Res. Comput. Eng. Technol., vol. Volume 2, no. Issue 2, 2013. [10] R. S. Chora, “Image Feature Extraction Techniques and Their Applications for CBIR and Biometrics Systems,” Int. J. Biol. Biomed. Eng., vol. 1, no. 1, 2007. [11] M. M. Dengsheng Zhang n GuojunLu, “A review on automatic image annotation techniques,” Pattern Recognit., pp. 346–362, 2012. [12] P. . Sahoo, S. Soltani, and a. K. . Wong, “A survey of thresholding techniques,” Comput. Vision, Graph. Image Process., vol. 41, no. 2, pp. 233–260, Feb. 1988. [13] N. OTSU, “A Threshold Selection Method from Gray-Level Histograms,” 2EEE Trans. SYSTREMS, MAN, Cybern., vol. Vol 9, 1979. [14] A. A. M. Al-kubati, J. A. M. Saif, and M. A. A. Taher, “Evaluation of Canny and Otsu Image Segmentation,” pp. 24–26, 2012. [15] M. Huang, W. Yu, and D. Zhu, “An Improved Image Segmentation Algorithm Based on the Otsu Method,” 2012 13th ACIS Int. Conf. Softw. Eng. Artif. Intell. Netw. Parallel/Distributed Comput., pp. 135–139, Aug. 2012. [16] Ruggeri F., Faltin F., and Kenett R., “Bayesian Networks,” Encyclopedia of Statistics in Quality & Reliability. Wiley & Sons, 2007. [17] P.Santhi and V. Murali Bhaskaran, “Improving the Performance of Data Mining Algorithms in Health Care Data,” IJCST, vol. 2, no. 3, pp. 152–157, 2011. [18] I. Guyon, “An Introduction to Variable and Feature Selection,” vol. 3, pp. 1157–1182, 2003. |
| spellingShingle | Comparison Of Image Classification Techniques Using Caltech 101 Dataset |
| summary | This paper presents the technique for the classification of single object images. First, this paper aims to introduce the efficient technique in order to classify single object image. Second, each single methods uses in order to propose the techniques were elaborated in this paper. It start from image segmentation, object identification, feature extraction, feature selection and classification. Finally, the best classifier that can provide the best results were identified. The efficiency of the proposed method is define by comparing the result of classification using two different datasets from author’s previous paper. The obligation for development of image classification has been improved due to remarkable growth in volume of images, as well as the widespread application in multiple fields. This paper explores the process of classifying images by the categories of object in the case of a large number of object categories. The use of a set of features to describe 2D shapes in low-level images has been proposed. The proposed technique aims a short and simple way to extract shape description before classifying the image. Using the Caltech 101 object recognition benchmark, classification was tested using four different classifiers; BayesNet, NaiveBayesUpdateable, Random Tree and IBk. Estimated accuracy was in the range from 58% to 99% (using 10-cross validation). By comparing with Amazon data, it is proved that the proposed model is more suitable for single object image. Amazon images give higher accuracy with the range from 80% to 99.48%. |
| title | Comparison Of Image Classification Techniques Using Caltech 101 Dataset |
| title_full | Comparison Of Image Classification Techniques Using Caltech 101 Dataset |
| title_fullStr | Comparison Of Image Classification Techniques Using Caltech 101 Dataset |
| title_full_unstemmed | Comparison Of Image Classification Techniques Using Caltech 101 Dataset |
| title_short | Comparison Of Image Classification Techniques Using Caltech 101 Dataset |
| title_sort | comparison of image classification techniques using caltech 101 dataset |