Personalized and Enhanced Hybridized Semantic Algorithm for web image retrieval incorporating ontology classification, strategic query expansion, and content-based analysis

被引：19

作者：

Deepak, Gerard ^{[1
]}

Priyadarshini, J. Sheeba ^{[2
]}

机构：

[1] Natl Inst Technol, Dept Comp Sci & Engn, Tiruchirappalli, Tamil Nadu, India

[2] St Aloysius Degree Coll & Post Grad Studies, Dept Comp Sci, Bangalore, Karnataka, India

来源：

COMPUTERS & ELECTRICAL ENGINEERING | 2018年 / 72卷

关键词：

Homonyms; Image retrieval; Ontologies; Recommendation systems; SVM; Web image mining; RECOMMENDATION;

D O I：

10.1016/j.compeleceng.2018.08.020

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Most of the existing web search systems are query-centric and are not user-centric. Mining images from the Web is a challenging task as it requires choosing the right methodology. A strategy that recommends images for homonyms and contextually similar terms have been proposed. The proposed system facilitates ontology modeling for homonyms and contextually related synonymous terms using description logics semantics and semantic similarity computation. An Enhanced Hybrid Semantic Algorithm that computes the semantic similarity and establishes dynamic OntoPath for easing the web image recommendation has been proposed. The proposed system classifies the ontologies using SVM and a Homonym LookUp directory. The methodology focuses on generating unique classes of images as an initial recommendation set. Based on the user click, strategic expansion of OntoPath takes place. Personalization is achieved by content-based analysis of the user click-through data. An overall accuracy of 95.87% is achieved by the proposed system. (C) 2018 Elsevier Ltd. All rights reserved.

引用

页码：14 / 25

页数：12

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