کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4970216 | 1365304 | 2016 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Linear discrimination dictionary learning for shape descriptors
ترجمه فارسی عنوان
یادگیری فرهنگ لغت خطی برای توصیفهای شکل
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کلمات کلیدی
تجزیه و تحلیل خطی خطی، یادگیری فرهنگ لغت بازیابی شکل،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی
The complexity and variation of 3D models have posed a lot of challenges in 3D shape retrieval area, for example, the invariant representation and retrieval of nonrigid and noisy 3D shapes. This paper proposed a supervised dictionary learning scheme called Linear Discrimination Dictionary Learning (LDDL) which can learn shape representations that are insensitive to 3D shape deformations in the same category and different for shapes from different categories in the meantime. Besides, it can extract the subtle differences between 3D shapes for fine-grained shapes. To be specific, in this paper, category-specific dictionaries are learnt to encode subtle visual differences of shapes among different categories, a shared dictionary is learnt to encode common patterns of shapes among all the categories; with the Linear Discriminant Analysis (LDA) constraint on the learnt descriptors, the new descriptors can have small within-class scatter and big between-class scatter. Our method is efficient in training and can obtain promising shape retrieval performance on representative shape benchmark datasets.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition Letters - Volume 83, Part 3, 1 November 2016, Pages 349-356
Journal: Pattern Recognition Letters - Volume 83, Part 3, 1 November 2016, Pages 349-356
نویسندگان
Meng Wang, Jin Xie, Fan Zhu, Yi Fang,