کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
561054 | 1451936 | 2017 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Domain adaptation from RGB-D to RGB images
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
پردازش سیگنال
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Domain adaptation from RGB-D to RGB images Domain adaptation from RGB-D to RGB images](/preview/png/561054.png)
چکیده انگلیسی
The introduction of depth cameras offers an opportunity to utilize the depth images to help the object recognition tasks. However, when our target tasks are classifying RGB images, how can we use the RGB-D images? To deal with this problem, we proposed a novel domain adaptation method by learning from RGB-D images in source domain to recognize RGB images in target domain, named DARDR. By introducing the cross modal constraint and the cross domain constraint, our DARDR can maximize the correlations between RGB and depth images in source domain and minimize the domain discrepancy across domains, simultaneously. We incorporate the two terms into the least-squares classifiers. Furthermore, a unified framework is presented to learn the classifier parameters. The advantage of our method is that the correlation between source RGB and depth images and the discrepancy between source and target data can be incorporated with the classifiers of source and target data. To evaluate our DARDR method, we apply it to five cross domain datasets. The experimental results demonstrate that our method can achieve competing performance against the state-of-art methods for object recognition and scene classification tasks.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Signal Processing - Volume 131, February 2017, Pages 27-35
Journal: Signal Processing - Volume 131, February 2017, Pages 27-35
نویسندگان
Xiao Li, Min Fang, Ju-Jie Zhang, Jinqiao Wu,