An effective similarity measure via genetic algorithm for content based image retrieval with extensive features
المؤلفون المشاركون
Syam, Baddeti
Rao, Yarravarapu Srinivasa
المصدر
The International Arab Journal of Information Technology
العدد
المجلد 10، العدد 2 (31 مارس/آذار 2013)10ص.
الناشر
تاريخ النشر
2013-03-31
دولة النشر
الأردن
عدد الصفحات
10
التخصصات الرئيسية
الموضوعات
الملخص EN
Recently, the construction of large datasets has been facilitated by the developments in data storage and image acquisition technologies.
In order to manage these datasets in an efficient manner development of suitable information systems are necessary.
Content-Based Image Retrieval is commonly utilized in most of the systems.
Based on image content, CBIR extracts images that are relevant to the given query image from large image databases.
Most of the CBIR systems available in the literature extract only concise feature sets that limit the retrieval efficiency.
In this paper, extensive features are extracted from the database images and stored in the feature library.
The extensive features set is comprised of shape feature along with the color, texture and the contourlet features, which are utilized in the previous work.
When a query image is given, the features are extracted in the similar fashion.
Subsequently, Genetic Algorithm-based similarity measure is performed between the query image features and the database image features.
The Squared Euclidean Distance (SED) aids the similarity measure in determining the Genetic Algorithm fitness.
Hence, from the Genetic Algorithm-based similarity measure, the database images that are relevant to the given query image are retrieved.
The proposed CBIR technique is evaluated by querying different images and the retrieval efficiency is evaluated by determining precision-recall values for the retrieval results.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Syam, Baddeti& Rao, Yarravarapu Srinivasa. 2013. An effective similarity measure via genetic algorithm for content based image retrieval with extensive features. The International Arab Journal of Information Technology،Vol. 10, no. 2.
https://search.emarefa.net/detail/BIM-311952
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Syam, Baddeti& Rao, Yarravarapu Srinivasa. An effective similarity measure via genetic algorithm for content based image retrieval with extensive features. The International Arab Journal of Information Technology Vol. 10, no. 2 (Mar. 2013).
https://search.emarefa.net/detail/BIM-311952
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Syam, Baddeti& Rao, Yarravarapu Srinivasa. An effective similarity measure via genetic algorithm for content based image retrieval with extensive features. The International Arab Journal of Information Technology. 2013. Vol. 10, no. 2.
https://search.emarefa.net/detail/BIM-311952
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references.
رقم السجل
BIM-311952
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