Vector space model adaptation and pseudo relevance feedback for content-based image retrieval

2017 ◽  
Vol 77 (5) ◽  
pp. 5475-5501 ◽  
Author(s):  
H. Karamti ◽  
M. Tmar ◽  
M. Visani ◽  
T. Urruty ◽  
F. Gargouri
Keyword(s):  
Image Retrieval ◽  
Vector Space ◽  
Relevance Feedback ◽  
Vector Space Model ◽  
Content Based Image Retrieval ◽  
Model Adaptation ◽  
Space Model ◽  
Pseudo Relevance Feedback

scholarly journals Improving the Results of Google Scholar Engine through Automatic Query Expansion Mechanism and Pseudo Re-ranking using MVRA

2018 ◽  
Vol 42 (2) ◽  
pp. 219-229
Author(s):  
Mawloud Mosbah
Keyword(s):  
Image Retrieval ◽  
Relevance Feedback ◽  
Query Expansion ◽  
Majority Voting ◽  
Google Scholar ◽  
Content Based Image Retrieval ◽  
Ranking Algorithm ◽  
Expansion Mechanism ◽  
Feedback Algorithm ◽  
Pseudo Relevance Feedback

In this paper, we address the enhancing of Google Scholar engine, in the context of text retrieval, through two mechanisms related to the interrogation protocol of that query expansion and reformulation. The both schemes are applied with re-ranking results using a pseudo relevance feedback algorithm that we have proposed previously in the context of Content based Image Retrieval (CBIR) namely Majority Voting Re-ranking Algorithm (MVRA). The experiments conducted using ten queries reveal very promising results in terms of effectiveness.

scholarly journals A new vector space model for image retrieval

2017 ◽  
Vol 112 ◽  
pp. 771-779 ◽  
Author(s):  
Hanen Karamti ◽  
Mohamed Tmar ◽  
Faiez Gargouri
Keyword(s):  
Image Retrieval ◽  
Vector Space ◽  
Vector Space Model ◽  
Space Model

scholarly journals Indonesian News Harvester and Recommender System

2017 ◽  
Vol 8 (3) ◽  
pp. 171 ◽  
Author(s):  
Adi Wibowo ◽  
Rolly Intan ◽  
Nydia Valentina
Keyword(s):  
Vector Space ◽  
Recommender System ◽  
System Architecture ◽  
Relevance Feedback ◽  
Vector Space Model ◽  
Space Model ◽  
News Websites ◽  
News Recommendation

To provide convenience for the user that frequently read the news, a system to gather, classify, and provide news from several news websites in one place was needed. This system utilized a recommender system to provide only relevant news to the user. This research proposed a system architecture that used vector space model, and Rocchio relevance feedback to provide specific news recommendation to user’s feedback. The results are that the proposed system architecture can achieve the goal by using five levels of feedback from the user. However, the time needed to gather news is increasing exponentially in line with the number of terms gathered from articles.

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