A Biological Text Retrieval System Based on Background Knowledge and User Feedback

2006 ◽  
pp. 50-64
Author(s):  
Meng Hu ◽  
Jiong Yang
Keyword(s):  
Retrieval System ◽  
Background Knowledge ◽  
Text Retrieval ◽  
User Feedback

Fuzzy Relevance Feedback in Image Retrieval for Color Feature Using Query Vector Modification Method

2010 ◽  
Vol 14 (1) ◽  
pp. 34-38 ◽  
Author(s):  
M. Rahmat Widyanto ◽  
◽  
Tatik Maftukhah ◽  
Keyword(s):  
Image Retrieval ◽  
Relevance Feedback ◽  
Euclidean Distance ◽  
Retrieval System ◽  
User Feedback ◽  
Image Similarity ◽  
Modification Method ◽  
Image Retrieval System ◽  
Membership Value ◽  
Image Computation

Fuzzy relevance feedback using Query Vector Modification (QVM) method in image retrieval is proposed. For feedback, the proposed six relevance levels are: “very relevant”, “relevant”, “few relevant”, “vague”, “not relevant”, and “very non relevant”. For computation of user feedback result, QVM method is proposed. The QVM method repeatedly reformulates the query vector through user feedback. The system derives the image similarity by computing the Euclidean distance, and computation of color parameter value by Red, Green, and Blue (RGB) color model. Five steps for fuzzy relevance feedback are: image similarity, output image, computation of membership value, feedback computation, and feedback result. Experiments used QVM method for six relevance levels. Fuzzy relevance feedback using QVM method gives higher precision value than conventional relevance feedback method. Experimental results show that the precision value improved by 28.56% and recall value improved 3.2% of conventional relevance feedback. That indicated performance Image Retrieval System can be improved by fuzzy relevance feedback using QVM method.

Adding compression to a full-text retrieval system

1995 ◽  
Vol 25 (8) ◽  
pp. 891-903 ◽  
Author(s):  
Justin Zobel ◽  
Alistair Moffat
Keyword(s):  
Full Text ◽  
Retrieval System ◽  
Text Retrieval ◽  
Full Text Retrieval

scholarly journals An Adaptable Connectionist Text-Retrieval System With Relevance Feedback

2007 ◽  
Vol 18 (6) ◽  
pp. 1597-1613 ◽  
Author(s):  
M.R. Azimi-Sadjadi ◽  
J. Salazar ◽  
S. Srinivasan ◽  
S. Sheedvash
Keyword(s):  
Relevance Feedback ◽  
Retrieval System ◽  
Text Retrieval

scholarly journals Radiocarbon Database: A Pilot Project

Radiocarbon ◽  
1983 ◽  
Vol 25 (2) ◽  
pp. 661-666 ◽  
Author(s):  
Steinar Gulliksen
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Retrieval System ◽  
Data Entry ◽  
Pilot Project ◽  
Text Retrieval ◽  
Data Input ◽  
Large Sets ◽  
Optical Character ◽  
Computer Storage

Computer storage and surveys of large sets of data should be an attractive technique for users of 14C dates. Our pilot project demonstrates the effectiveness of a text retrieval system, NOVA STATUS. A small database comprising ca 100 dates, selected from results of the Trondheim 14C laboratory, is generated. Data entry to the computer is made by feeding typewritten forms through a document reader capable of optical character recognition. A text retrieval system allows data input to be in a flexible format. Program systems for text retrieval are in common use and easily implemented for a 14C database.

scholarly journals A Full Text Retrieval System in a Digital Library Environment

2016 ◽  
Vol 08 (01) ◽  
pp. 1-8 ◽  
Author(s):  
Kehinde Daniel Aruleba ◽  
Dipo Theophilus Akomolafe ◽  
Babajide Afeni
Keyword(s):  
Digital Library ◽  
Full Text ◽  
Retrieval System ◽  
Text Retrieval ◽  
Full Text Retrieval

Design and Evaluation of a Content-Based Image Retrieval System

2011 ◽  
pp. 125-151 ◽  
Author(s):  
David Squire ◽  
Henning Muller ◽  
Wolfgang Muller ◽  
Stephane Marchand-Maillet ◽  
Thierry Pun
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Image Understanding ◽  
Text Retrieval ◽  
Multimedia Databases ◽  
Multimedia Retrieval ◽  
Context Dependency ◽  
Content Based Image Retrieval ◽  
Classical Text ◽  
Image Retrieval System

The growth in size and accessibility of multimedia databases has changed our approach to information retrieval. Classical text-based systems show their limitations in the context of multimedia retrieval. In this chapter, we address the problem of conceiving and evaluating a content-based image retrieval system. First, we investigate the use of the query-by-example (QBE) paradigm as a base paradigm for the development of a content-based image retrieval system (CBIRS). We show that it should be considered as a complement to the classical textual-based paradigms. We then evaluate the capabilities of the most up-to-date computer vision techniques in contributing to the realisation of such a system. Further, beyond the necessity of accurate image understanding techniques, we show that the amount of the data involved in the process of describing image content should also be considered as an important issue. This aspect of our study is largely based on the experience acquired by the text retrieval (TR) community, which we adapt to the context of CBIR. Similarly, the text retrieval community has also developed significant experience in evaluating retrieval systems, where judgements include subjectivity and context dependency. Extending this experience, we study a coherent framework for performing the evaluation of a CBIRS. As a practical example, we user our Viper CBIR system, using a novel communication protocol called MRML (Multimedia Retrieval Markup Language) to pinpoint the importance of the sharing of resources in facilitating the evaluation and therefore the development of CBIRS.

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