Home > Archive > 2014 > Volume 4 Number 6 (Dec. 2014) >
IJIET 2014 Vol.4(6): 487-490 ISSN: 2010-3689
DOI: 10.7763/IJIET.2014.V4.456

Combination of Keyword and Visual Features for Web Image Retrieval System

Nyein Myint Myint Aung

Abstract—This paper presents the implementation of web image retrieval system using keyword-based search and visual image features. In order to correctly correlate terms to a web image, the associated text of the web image is partitioned into text blocks according to the structure of the text with respect to the web images. Then, keywords are extracted and stored in image indexing database which will later be used in keyword based retrieval. When user enters keyword, result images are generated by image indexing and searching algorithms. Most of the web image search systems are based on only keyword based searches. But most of the result images in the keyword search are not relevant to the query. To reduce the influence of those irrelevant images, visual image features are used. Firstly, image features are extracted and then they are stored in the image indexing database. And then these features are used to cluster images for relevant and non-relevant. Combination of keyword search and visual feature extraction will result in producing more relevant images by removing non-relevant images. Extracting visual features can improve the system performance.

Index Terms—Image retrieval system, keyword-based search, visual image features.

Nyein Myint Myint Aung is with University of Technology, Yatanarpon Cyber City, Myanmar (e-mail: thae.thae.star@gmail.com).


Cite: Nyein Myint Myint Aung, "Combination of Keyword and Visual Features for Web Image Retrieval System," International Journal of Information and Education Technology vol. 4, no. 6, pp. 487-490, 2014.

General Information

  • ISSN: 2010-3689 (Online)
  • Abbreviated Title: Int. J. Inf. Educ. Technol.
  • Frequency: Monthly
  • DOI: 10.18178/IJIET
  • Editor-in-Chief: Prof. Dr. Steve Thatcher
  • Executive Editor: Ms. Nancy Y. Liu
  • Abstracting/ Indexing: Scopus (CiteScore 2022: 2.0), INSPEC (IET), UGC-CARE List (India), CNKI, EBSCO, Google Scholar
  • E-mail: ijiet@ejournal.net


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