tudi Kelayakan dan Perancangan Aplikasi Pencarian Buku pada Katalog Perpustakaan Menggunakan Rocchio Relevance Feedback

2017 ◽  
Vol 8 (1) ◽  
Author(s):  
Albert Albert ◽  
Marcel Bonar Kristanda ◽  
Seng Hansun
Keyword(s):  
Relevance Feedback ◽  
Mobile Application ◽  
Library Catalog ◽  
Feedback Method ◽  
Relevant Calculation

A catalog is a register of all bibliographic items found in a library. A bibliographic item can be any information entity. The library catalog has evolved from manual, website based catalog to mobile catalog. Unfortunately, there are still many obstacles in the results of library catalog search, including the relevant results of documents based on input from the user. The purpose of this research is to make the library catalog based on mobile application in android using relevant calculation used rocchio relevance feedback method. Terms— android, library, library catalog, mobile, rocchio.

Rancang Bangun Aplikasi UMN Library Catalog Menggunakan Metode Rocchio Relevance Feedback

2018 ◽  
Vol 9 (1) ◽  
pp. 9-17
Author(s):  
Marcel Bonar Kristanda ◽  
Seng Hansun ◽  
Albert Albert
Keyword(s):  
Information System ◽  
User Experience ◽  
Relevance Feedback ◽  
Android Application ◽  
Library Catalog ◽  
Relevant Result ◽  
User Query ◽  
Index Terms ◽  
Feedback Method ◽  
F Measure

Library catalog is a documentation or list of all library collections. Unfortunately, there is a problem identified in the process of searching a book inside library catalog in Universitas Multimedia Nusantara’s library information system regarding the relevant result based on user query input. This research aims to design and build a library catalog application on Android platform in order to increase the relvancy of searching result in a database using calculated Rocchio Relevance Feedback method along with user experience measurement. User experience analysis result presented a good respond with 91.18% score based by all factor and relevance value present 71.43% precision, 100% recall, and 83.33% F-Measure. Differences of relevant results between the Senayan Library Information system (SLiMS) and the new Android application ranged at 36.11%. Therefore, this Android application proved to give relevant result based on relevance rank. Index Terms—Rocchio, Relevance, Feedback, Pencarian, Buku, Aplikasi, Android, Perpustakaan.

A novel relevance feedback method for CBIR

2018 ◽  
Vol 21 (6) ◽  
pp. 1505-1522 ◽  
Author(s):  
Yunbo Rao ◽  
Wei Liu ◽  
Bojiang Fan ◽  
Jiali Song ◽  
Yang Yang
Keyword(s):  
Relevance Feedback ◽  
Feedback Method

Research on Related Technology of Content-Based Image Retrieval

2013 ◽  
Vol 448-453 ◽  
pp. 3616-3620
Author(s):  
Yu Bing Dong ◽  
Ming Jing Li ◽  
Bai Chuan Li
Keyword(s):  
Image Retrieval ◽  
Relevance Feedback ◽  
Image Feature ◽  
Content Based Image Retrieval ◽  
The Novel ◽  
Effective Solution ◽  
Retrieval Accuracy ◽  
Novel Method ◽  
Feedback Method ◽  
High Level

Content-Based Image Retrieval (CBIR) system existed a gap between high-level concepts and low-level features. As an effective solution, the Relevance Feedback (RF) technique has been used on many CBIR systems to improve the retrieval precision. In order to further improve convergence speed and retrieval accuracy, a novel relevance feedback method was proposed. According to feedback from user, image feature was weighted and adjusted in the novel method.

An implicit relevance feedback method for CBIR with real-time eye tracking

2015 ◽  
Vol 75 (5) ◽  
pp. 2595-2611 ◽  
Author(s):  
Qingyong Li ◽  
Mei Tian ◽  
Jun Liu ◽  
Jinrui Sun
Keyword(s):  
Eye Tracking ◽  
Real Time ◽  
Relevance Feedback ◽  
Feedback Method

EXTRACTING SALIENT REGIONS AND LEARNING IMPORTANCE SCORES IN REGION-BASED IMAGE RETRIEVAL

2003 ◽  
Vol 17 (08) ◽  
pp. 1349-1367 ◽  
Author(s):  
BYOUNGCHUL KO ◽  
HYERAN BYUN
Keyword(s):  
Image Retrieval ◽  
Relevance Feedback ◽  
Retrieval System ◽  
Retrieval Performance ◽  
Matching Process ◽  
Similar Images ◽  
Salient Regions ◽  
Feedback Method ◽  
Successful Retrieval ◽  
Feedback Algorithm

In this paper, we propose a new method for extracting salient regions and learning their importance scores in region-based image retrieval. In Region-Based Image Retrieval (RBIR), not all the regions are important for retrieving similar images and rather, in retrieval, the user is often interested in performing a query on only one or a few regions rather than the whole image. Therefore, for a successful retrieval system, it is an important issue to specify which regions are important for retrieving an image. To extract salient regions from images automatically, we make three assumptions and determine salient regions with their importance scores. In this paper, we apply the relevance feedback algorithm to the matching process as two different purposes: one is for updating importance scores of salient regions and the other is for updating weights of feature vectors. By using our relevance feedback method, the matching process can improve retrieval performance interactively and allow progressive refinement of query results according to the user's feedback action. Through experiments and comparison with other methods, our proposed method shows good performance as well as easy and semantic interface for region-based image retrieval. The efficacy of our method is validated using a set of 3000 images from Corel-photo CD.

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