An Analysis and Application of Fast Nonnegative Orthogonal Matching Pursuit for Image Categorization in Deep Networks
المؤلفون المشاركون
Wang, Bo
Guo, Jichang
Zhang, Yan
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2015، العدد 2015 (31 ديسمبر/كانون الأول 2015)، ص ص. 1-9، 9ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2015-07-02
دولة النشر
مصر
عدد الصفحات
9
التخصصات الرئيسية
الملخص EN
Nonnegative orthogonal matching pursuit (NOMP) has been proven to be a more stable encoder for unsupervised sparse representation learning.
However, previous research has shown that NOMP is suboptimal in terms of computational cost, as the coefficients selection and refinement using nonnegative least squares (NNLS) have been divided into two separate steps.
It is found that this problem severely reduces the efficiency of encoding for large-scale image patches.
In this work, we study fast nonnegative OMP (FNOMP) as an efficient encoder which can be accelerated by the implementation of QR factorization and iterations of coefficients in deep networks for full-size image categorization task.
It is analyzed and demonstrated that using relatively simple gain-shape vector quantization for training dictionary, FNOMP not only performs more efficiently than NOMP for encoding but also significantly improves the classification accuracy compared to OMP based algorithm.
In addition, FNOMP based algorithm is superior to other state-of-the-art methods on several publicly available benchmarks, that is, Oxford Flowers, UIUC-Sports, and Caltech101.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Wang, Bo& Guo, Jichang& Zhang, Yan. 2015. An Analysis and Application of Fast Nonnegative Orthogonal Matching Pursuit for Image Categorization in Deep Networks. Mathematical Problems in Engineering،Vol. 2015, no. 2015, pp.1-9.
https://search.emarefa.net/detail/BIM-1073139
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Wang, Bo…[et al.]. An Analysis and Application of Fast Nonnegative Orthogonal Matching Pursuit for Image Categorization in Deep Networks. Mathematical Problems in Engineering No. 2015 (2015), pp.1-9.
https://search.emarefa.net/detail/BIM-1073139
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Wang, Bo& Guo, Jichang& Zhang, Yan. An Analysis and Application of Fast Nonnegative Orthogonal Matching Pursuit for Image Categorization in Deep Networks. Mathematical Problems in Engineering. 2015. Vol. 2015, no. 2015, pp.1-9.
https://search.emarefa.net/detail/BIM-1073139
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1073139
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر