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Paper Details

Paper Title
We recommend making use of strong semantic relationship within graph for the image search re-ranking and exploit semantic attributes in support of image search re-ranking and on the basis of classifiers for the entire predefined attributes, each of the image is symbolised by an attribute feature that includes responses from these classifiers. Semantic attributes have received more consideration in the recent times in which efficiency was verified in broad applications. Semantic attributes is considered as mid-level semantic preserving notion. We introduce a hyper-graph to model relationship among images by means of integration of low-level features as well as attribute features and later hyper-graph ranking is carried out to order the images and its fundamental principle is that visually similar images include related scores of ranking. We introduce attribute-assisted re-ranking technique that is based on hyper-graph learning and we instruct numerous classifiers for the entire pre-defined attributes and every image is represented by means of attribute feature that includes responses from these classifiers.
Semantic attributes, Hyper-graph learning, Image search re-ranking.
Others Details
Paper Id : 76376
Author Name : veladandi divya
Volume/Issue No : Volume 04 Issue 11
Page No : 260-262
DOI Number : DOI:10.21090/IJAERD.76376
Publication Date : 2017-11-14
License : This work is licensed under a Creative Commons Attribution 4.0 International License.
website :
Impact Factor : 4.72, SJIF-2016
ISSN Details : eISSN: 2348-4470, pISSN:2348-6406