کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4969902 | 1449983 | 2017 | 36 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Neural Bag-of-Features learning
ترجمه فارسی عنوان
یادگیری ویژگی های کیفی عصبی
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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, a neural learning architecture for the well-known Bag-of-Features (BoF) model, called Neural Bag-of-Features, is proposed. The Neural BoF model is formulated in two neural layers: a Radial Basis Function (RBF) layer and an accumulation layer. The ability of the Neural BoF model to improve the classification performance is demonstrated using four datasets, including a large-scale dataset, and five different feature types. The gains are two-fold: the classification accuracy increases and, at the same time, smaller networks can be used, reducing the required training and testing time. Furthermore, the Neural BoF natively supports training and classifying from feature streams. This allows the proposed method to efficiently scale to large datasets. The streaming process can also be used to introduce noise and reduce the over-fitting of the network. Finally, the Neural BoF provides a framework that can model and extend the dictionary learning methodology.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 64, April 2017, Pages 277-294
Journal: Pattern Recognition - Volume 64, April 2017, Pages 277-294
نویسندگان
Nikolaos Passalis, Anastasios Tefas,