کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6939635 | 1449972 | 2018 | 32 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Person re-identification via integrating patch-based metric learning and local salience learning
ترجمه فارسی عنوان
شناسایی فرد از طریق یکپارچه سازی یادگیری متریک مبتنی بر پچ و یادگیری برجسته محلی
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی
In this paper, aiming at improving the generalization capability, we propose a cross-dataset person re-identification framework via integrating patch-based metric learning and local salience learning. Firstly, Convolution Neural Network(CNN) features are extracted to represent patches of a person. Secondly, only two positive patch-pairs are chosen and input into a Large Margin Nearest Neighbour(LMNN) network to learn two patch-based metric matrices for feature projection respectively. Thirdly, according to projected new features, a local salience learning algorithm based on Kmeans clustering is proposed to train the weights of patches. Finally, the similarity of image-pair is computed by a weighted summing of all patches. The experimental results indicate that the proposed method outperforms existing conventional approaches based on hand-crafted features and achieves a comparable performance with most recent CNN-based methods, which demonstrates our method's effectiveness and practicality. It does not need a large-scale labeled training dataset, and has a high matching rate with a low computation complexity.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 75, March 2018, Pages 90-98
Journal: Pattern Recognition - Volume 75, March 2018, Pages 90-98
نویسندگان
Zhicheng Zhao, Binlin Zhao, Fei Su,