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Object Detection With DoG Scale-Space: A Multiple Kernel Learning Approach.
- Source :
-
IEEE Transactions on Image Processing . Aug2012, Vol. 21 Issue 8, p3744-3756. 13p. - Publication Year :
- 2012
-
Abstract
- Difference of Gaussians (DoG) scale-space for an image is a significant way to generate features for object detection and classification. While applying DoG scale-space features for object detection/classification, we face two inevitable issues: dealing with high-dimensional data and selecting/weighting of proper scales. The scale selection process is mostly ad-hoc to present. In this paper, we propose a multiple kernel learning (MKL) method for both DoG scale selection/weighting and dealing with high-dimensional scale-space data. We design a novel shift invariant kernel function for DoG scale-space. To select only the useful scales in the DoG scale-space, a novel framework of MKL is also proposed. We utilize a 1-norm support vector machine (SVM) in the MKL optimization problem for sparse weighting of scales from DoG scale-space. The optimized data-dependent kernel accommodates only a few scales that are most discriminatory according to the large margin principle. With a 2-norm SVM, this learned kernel is applied to a challenging detection problem in oil sand mining: to detect large lumps in oil sand videos. We tested our method on several challenging oil sand data sets. Our method yields encouraging results on these difficult-to-process images and compares favorably against other popular multiple kernel methods. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10577149
- Volume :
- 21
- Issue :
- 8
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Image Processing
- Publication Type :
- Academic Journal
- Accession number :
- 77874792
- Full Text :
- https://doi.org/10.1109/TIP.2012.2192130