Mining Regional Co-Occurrence Patterns for Image Classification
المؤلفون المشاركون
Hu, Xiao Peng
Xu, Lijuan
Yang, Yan
Ji, Zhihang
Wu, Sining
Wang, Fan
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-14، 14ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-09-25
دولة النشر
مصر
عدد الصفحات
14
التخصصات الرئيسية
الملخص EN
In the context of image classification, bag-of-visual-words mode is widely used for image representation.
In recent years several works have aimed at exploiting color or spatial information to improve the representation.
In this paper two kinds of representation vectors, namely, Global Color Co-occurrence Vector (GCCV) and Local Color Co-occurrence Vector (LCCV), are proposed.
Both of them make use of the color and co-occurrence information of the superpixels in an image.
GCCV describes the global statistical distribution of the colorful superpixels with embedding the spatial information between them.
By this way, it is capable of capturing the color and structure information in large scale.
Unlike the GCCV, LCCV, which is embedded in the Riemannian manifold space, reflects the color information within the superpixels in detail.
It records the higher-order distribution of the color between the superpixels within a neighborhood by aggregating the co-occurrence information in the second-order pooling way.
In the experiment, we incorporate the two proposed representation vectors with feature vector like LLC or CNN by Multiple Kernel Learning (MKL) technology.
Several challenging datasets for visual classification are tested on the novel framework, and experimental results demonstrate the effectiveness of the proposed method.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Ji, Zhihang& Wu, Sining& Wang, Fan& Xu, Lijuan& Yang, Yan& Hu, Xiao Peng. 2018. Mining Regional Co-Occurrence Patterns for Image Classification. Mathematical Problems in Engineering،Vol. 2018, no. 2018, pp.1-14.
https://search.emarefa.net/detail/BIM-1207754
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Ji, Zhihang…[et al.]. Mining Regional Co-Occurrence Patterns for Image Classification. Mathematical Problems in Engineering No. 2018 (2018), pp.1-14.
https://search.emarefa.net/detail/BIM-1207754
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Ji, Zhihang& Wu, Sining& Wang, Fan& Xu, Lijuan& Yang, Yan& Hu, Xiao Peng. Mining Regional Co-Occurrence Patterns for Image Classification. Mathematical Problems in Engineering. 2018. Vol. 2018, no. 2018, pp.1-14.
https://search.emarefa.net/detail/BIM-1207754
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1207754
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