کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10360661 | 869878 | 2005 | 12 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Visual object recognition using probabilistic kernel subspace similarity
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Visual object recognition using probabilistic kernel subspace similarity Visual object recognition using probabilistic kernel subspace similarity](/preview/png/10360661.png)
چکیده انگلیسی
Probabilistic subspace similarity-based face matching is an efficient face recognition algorithm proposed by Moghaddam et al. It makes one basic assumption: the intra-class face image set spans a linear space. However, there are yet no rational geometric interpretations of the similarity under that assumption. This paper investigates two subjects. First, we present one interpretation of the intra-class linear subspace assumption from the perspective of manifold analysis, and thus discover the geometric nature of the similarity. Second, we also note that the linear subspace assumption does not hold in some cases, and generalize it to nonlinear cases by introducing kernel tricks. The proposed model is named probabilistic kernel subspace similarity (PKSS). Experiments on synthetic data and real visual object recognition tasks show that PKSS can achieve promising performance, and outperform many other current popular object recognition algorithms.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 38, Issue 7, July 2005, Pages 997-1008
Journal: Pattern Recognition - Volume 38, Issue 7, July 2005, Pages 997-1008
نویسندگان
Jianguo Lee, Jingdong Wang, Changshui Zhang, Zhaoqi Bian,