Hybrid Relative Attributes Based on Sparse Coding for Zero-Shot Image Classification
المؤلفون المشاركون
Yun, Xiao
Lu, Nannan
Sun, Yan-jing
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-13، 13ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-02-21
دولة النشر
مصر
عدد الصفحات
13
التخصصات الرئيسية
الملخص EN
As a specific case of image recognition, zero-shot image classification is difficult to solve since its training set cannot cover all the categories of the testing set.
From the view point of human vision recognition, the objects can be recognized through the visible and nameable description to the properties.
To be the semantic description of the object property, attributes can be taken as a bridge between the seen and unseen categories, which are capable of using into zero-shot image classification.
There are mainly binary attributes and relative attributes for zero-shot classification, where the relative attributes have the ability to catch more general sematic relationship than the binary ones.
But relative attributes do not always work in zero-shot classification for those categories having similar relative strength attributes.
Aiming at solving the defect of the relative attributes in describing the similar categories, we propose to construct the Hybrid Relative Attributes based on Sparse Coding (SC-HRA).
First, sparse coding is implemented on low-level features to get nonsemantic relative attributes, which are the necessary complement to the existing relative attributes.
After that, they are integrated with the relative attributes to form the hybrid relative attributes (HRA).
HRA ranking functions are then learned by the relative attribute learning.
Finally, the class label is obtained according to the predicted ranking results of HRA and the ranking relations of HRA among the categories.
To verify the effectiveness of SC-HRA, the extensive experiments are conducted on the datasets of faces and natural scenes.
The results show that SC-HRA acquires the higher classification accuracy and AUC value.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Lu, Nannan& Sun, Yan-jing& Yun, Xiao. 2019. Hybrid Relative Attributes Based on Sparse Coding for Zero-Shot Image Classification. Mathematical Problems in Engineering،Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1196978
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Lu, Nannan…[et al.]. Hybrid Relative Attributes Based on Sparse Coding for Zero-Shot Image Classification. Mathematical Problems in Engineering No. 2019 (2019), pp.1-13.
https://search.emarefa.net/detail/BIM-1196978
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Lu, Nannan& Sun, Yan-jing& Yun, Xiao. Hybrid Relative Attributes Based on Sparse Coding for Zero-Shot Image Classification. Mathematical Problems in Engineering. 2019. Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1196978
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1196978
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