Öz
In the field of machine learning, which values / data labeling or recognition is done by pattern recognition. Visual object classification is an example of pattern recognition, which attempts prompt to assign each object to one of a given set of object classes. The basic elements of the process of pattern recognition, feature extraction, feature selection and classification. The complexity of feature selection/extraction is, because of its non-monotonous character, an optimization problem. The process of feature extraction, pattern characteristic feature is eliminated and the acquisition of a certain amount of irrelevant information is provided dimensionality reduction. In the fields of machine learning and statistics, feature selection algorithms are known the choice of variable selection or additional subset of variables. For the most part of visual object classification methods use bag of words model for image representation with image features. In this method, patches extracted from images are described by different shape and texture descriptors such as SIFT, LBP, LTP, SURF, etc. In this paper we introduce a new descriptor based on weighted histograms of angle between two vectors of local based PCA transform. We compare the classification accuracies obtained by using the proposed descriptor to the ones obtained by other well-known descriptors on Caltech-4 and Coil-100 data sets. Experimental results show that our proposed descriptor provides good accuracies indicating that PCA based local descriptor captures important characteristics of images that are useful for classification. When we described image representations obtained by PCA based descriptor with the representations obtained by other detection of keypoints, results even get better suggesting that tested descriptors encode differential complementary information.
Anahtar Kelimeler
descriptor feature extraction visual object classification PCA method bag of words model
Kaynakça
- G. Csurka, C. R. Dance, L. Fan, J. Willamowski, C. Bray, “Visual categorization with bags of keypoints”, ECCV Workshop on Statistical Learning for Computer Vision, 2004.
- F. Moosman, E. Nowak, and F. Jurie, “Randomized clustering forests for image classification”, IEEE Transactions on PAMI, vol. 30, pp. 1632-1646, 2008.
- F. Jurie and B. Triggs, “Creating efficient codebooks for visual recognition”, ICCV, 2005.
- D. G. Lowe, “Distinctive image features from scale-invariant keypoints”, International Journal of Computer Vision, vol. 60, 2004.
- E. Nowak, F. Jurie, and B. Triggs, “Sampling strategies for bag-of-features image classification”, ECCV, 2006.
- T. Leung, J. Malik, “Representing and recognizing the visual appearance of materials using three-dimensional textons”, International Journal of Computer Vision, vol. 43, pp. 29-44, 2001.
- K. Barnard, P. Duygulu, R. Guru, P. Gabbur, and D. Forsyth, “The effects of segmentation and feature choice in a translation model of object recognition”, CVPR, 2003.
- P. Koniusz and K. Mikolajczyk, “On a quest for image descriptors based on unsupervised segmentation maps”, International Conference on Pattern Recognition, 2010.
- J. Sivic, B. C. Russell, A. A. Efros, A. Zisserman, and W. T. Freeman, “Discovering object categories in image collections”, CVPR, 2005.
- A. A. Ursani, K. Kpalma, and J. Ronsin, “Texture features based on Fourier transform and Gabor filters: an empirical comparison”, International Conference on Machine Vision, 2007.
- M. Heikkila, M.Pietikainen, and C. Schmid, “Description of interest regions with local binary patterns”, Pattern Recognition, vol. 42, pp. 425-436, 2009.
- F. Zhou, J.-F. Feng, and Q.-Y. Shi, “Texture feature based on local Fourier transform”, International Conference on Image Processing, 2001.
- A. A. Ursani, K. Kpalma, and J. Ronsin, “Texture features based on local Fourier histogram: self-compensation against rotation”, Journal of Electronic Imaging, 2008.
- T. Ahonen, J. Matas, C. He, and M. Pietikainen, “Rotation invariant image description with local binary pattern histogram Fourier features”, SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis, 2009.
- H. Bay, A. Ess, T. Tuytelaars, and L. V. Gool, “Surf: Speeded up robust features”, Computer Vision and Image Understanding, vol. 110, pp. 346-359, 2008.
- S. Belongie, J. Malik, and J. Puzicha, “Shape matching and object recognition using shape contexts”, IEEE Transactions on PAMI, vol. 24, pp. 509-521, 2002.
- N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection”, CVPR, 2005.
- X. Tan and B. Triggs, “Enhanced local texture feature sets for face recognition under difficult lighting conditions”, IEEE Transactions on Image Processing, vol. 19, pp. 1635-1650, 2010.
- H. Harzallah, F. Jurie, and C. Schmid, “Combining efficient object localization and image classification”, ICCV, 2009.
- R. Fergus, L. Fei-Fei, P. Perona, and A. Zisserman, “Learning object categories from Google’s image search”, ICCV, 2005.
- D. Nister and H. Stewenius, “Scalable recognition with a vocabulary tree”, CVPR, 2006.
- Available at http://www.vision.caltech.edu/html-files/ archive.html.
- Available at http://www.cs.columbia.edu/CAVE/software/ softlib/coil-100.php
Öz
Kaynakça
- G. Csurka, C. R. Dance, L. Fan, J. Willamowski, C. Bray, “Visual categorization with bags of keypoints”, ECCV Workshop on Statistical Learning for Computer Vision, 2004.
- F. Moosman, E. Nowak, and F. Jurie, “Randomized clustering forests for image classification”, IEEE Transactions on PAMI, vol. 30, pp. 1632-1646, 2008.
- F. Jurie and B. Triggs, “Creating efficient codebooks for visual recognition”, ICCV, 2005.
- D. G. Lowe, “Distinctive image features from scale-invariant keypoints”, International Journal of Computer Vision, vol. 60, 2004.
- E. Nowak, F. Jurie, and B. Triggs, “Sampling strategies for bag-of-features image classification”, ECCV, 2006.
- T. Leung, J. Malik, “Representing and recognizing the visual appearance of materials using three-dimensional textons”, International Journal of Computer Vision, vol. 43, pp. 29-44, 2001.
- K. Barnard, P. Duygulu, R. Guru, P. Gabbur, and D. Forsyth, “The effects of segmentation and feature choice in a translation model of object recognition”, CVPR, 2003.
- P. Koniusz and K. Mikolajczyk, “On a quest for image descriptors based on unsupervised segmentation maps”, International Conference on Pattern Recognition, 2010.
- J. Sivic, B. C. Russell, A. A. Efros, A. Zisserman, and W. T. Freeman, “Discovering object categories in image collections”, CVPR, 2005.
- A. A. Ursani, K. Kpalma, and J. Ronsin, “Texture features based on Fourier transform and Gabor filters: an empirical comparison”, International Conference on Machine Vision, 2007.
- M. Heikkila, M.Pietikainen, and C. Schmid, “Description of interest regions with local binary patterns”, Pattern Recognition, vol. 42, pp. 425-436, 2009.
- F. Zhou, J.-F. Feng, and Q.-Y. Shi, “Texture feature based on local Fourier transform”, International Conference on Image Processing, 2001.
- A. A. Ursani, K. Kpalma, and J. Ronsin, “Texture features based on local Fourier histogram: self-compensation against rotation”, Journal of Electronic Imaging, 2008.
- T. Ahonen, J. Matas, C. He, and M. Pietikainen, “Rotation invariant image description with local binary pattern histogram Fourier features”, SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis, 2009.
- H. Bay, A. Ess, T. Tuytelaars, and L. V. Gool, “Surf: Speeded up robust features”, Computer Vision and Image Understanding, vol. 110, pp. 346-359, 2008.
- S. Belongie, J. Malik, and J. Puzicha, “Shape matching and object recognition using shape contexts”, IEEE Transactions on PAMI, vol. 24, pp. 509-521, 2002.
- N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection”, CVPR, 2005.
- X. Tan and B. Triggs, “Enhanced local texture feature sets for face recognition under difficult lighting conditions”, IEEE Transactions on Image Processing, vol. 19, pp. 1635-1650, 2010.
- H. Harzallah, F. Jurie, and C. Schmid, “Combining efficient object localization and image classification”, ICCV, 2009.
- R. Fergus, L. Fei-Fei, P. Perona, and A. Zisserman, “Learning object categories from Google’s image search”, ICCV, 2005.
- D. Nister and H. Stewenius, “Scalable recognition with a vocabulary tree”, CVPR, 2006.
- Available at http://www.vision.caltech.edu/html-files/ archive.html.
- Available at http://www.cs.columbia.edu/CAVE/software/ softlib/coil-100.php
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Bölüm | Research Article |
| Yazarlar | |
| Yayımlanma Tarihi | 13 Kasım 2015 |
| Yayımlandığı Sayı | Yıl 2015 Cilt: 3 Sayı: 3 |