Sparsity Based Locality-Sensitive Discriminative Dictionary Learning for Video Semantic Analysis
المؤلفون المشاركون
Benuwa, Ben-Bright
Zhan, Yongzhao
Ghansah, Benjamin
Ansah, Ernest K.
Sarkodie, Andriana
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-11، 11ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-08-05
دولة النشر
مصر
عدد الصفحات
11
التخصصات الرئيسية
الملخص EN
Dictionary learning (DL) and sparse representation (SR) based classifiers have greatly impacted the classification performance and have had good recognition rate on image data.
In video semantic analysis (VSA), the local structure of video data contains more vital discriminative information needed for classification.
However, this has not been fully exploited by the current DL based approaches.
Besides, similar coding findings are not being realized from video features with the same video category.
Based on the issues stated afore, a novel learning algorithm, called sparsity based locality-sensitive discriminative dictionary learning (SLSDDL) for VSA is proposed in this paper.
In the proposed algorithm, a discriminant loss function for the category based on sparse coding of the sparse coefficients is introduced into structure of locality-sensitive dictionary learning (LSDL) algorithm.
Finally, the sparse coefficients for the testing video feature sample are solved by the optimized method of SLSDDL and the classification result for video semantic is obtained by minimizing the error between the original and reconstructed samples.
The experiment results show that the proposed SLSDDL significantly improves the performance of video semantic detection compared with the comparative state-of-the-art approaches.
Moreover, the robustness to various diverse environments in video is also demonstrated, which proves the universality of the novel approach.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Benuwa, Ben-Bright& Zhan, Yongzhao& Ghansah, Benjamin& Ansah, Ernest K.& Sarkodie, Andriana. 2018. Sparsity Based Locality-Sensitive Discriminative Dictionary Learning for Video Semantic Analysis. Mathematical Problems in Engineering،Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1209607
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Benuwa, Ben-Bright…[et al.]. Sparsity Based Locality-Sensitive Discriminative Dictionary Learning for Video Semantic Analysis. Mathematical Problems in Engineering No. 2018 (2018), pp.1-11.
https://search.emarefa.net/detail/BIM-1209607
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Benuwa, Ben-Bright& Zhan, Yongzhao& Ghansah, Benjamin& Ansah, Ernest K.& Sarkodie, Andriana. Sparsity Based Locality-Sensitive Discriminative Dictionary Learning for Video Semantic Analysis. Mathematical Problems in Engineering. 2018. Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1209607
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1209607
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