Skip to main content
SpringerLink
Log in

The study on content based multimedia data retrieval system

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Of late, advance in hardware and communications technology has been rapidly increasing the demand for diverse multimedia information, which, including all image, audio, video, text, numerical data, etc., should be designed to excel the existing information processing system in the functions of data storage, search, transmission, display, etc. The newest image retrieval system is gradually being converted from text-based into content-based retrieval, which uses the image content itself as features. In content-based retrieval, how to combine the color, shape, layout, texture, etc. used for describing each image or object is considered an important element. The existing method has chiefly used histogram out of the content-based image method using color information, but this has a drawback in being sensitive to brightness of light and the object size in the image. Thus, the present methods is intended to design and implement a system that can retrieve images similar to the query image from image database without losing image information in the use of color features.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Bassmann H, Besslich PW (1995) Ad Oculos: digital image processing. International Thomson Publishing

  2. El-Naqa I, Yang Y, Galatsanos NP, Nishikawa RM, Wernick MN (2004) A similarity learning approach to content-based image retrieval: application to digital mammography. Med Imaging 23(10):1233–1244

    Article  Google Scholar 

  3. Ghanem SM, Ismail MA, Omar SG (2010) System design of a super-peer network for content-based image retrieval. Computer and Information Technology 2486–2493

  4. Kaplan LM, Murenzi R, Namuduri KR (1997) Fast texture dadabase retrieval using extended fractal features. Proc SPIE Conf On Electric Imaging: Storage & Retrieval for Image and Video Database IV 3312:162–173, San Jose, CA, 1997

    Google Scholar 

  5. Kotoulas L, Andreadis I (2003) Colour histogram content-based image retrieval and hardware implementation. Circuits Devices Syst 150(5):387–393

    Article  Google Scholar 

  6. Leauhatong T, Atsuta K, Kondo S (2007) A new content-based image retrieval using color correlogram and inner product metric. Image Analysis for Multimedia Interactive Services 33–36

  7. Li R, Zhang YaFei, Zining Lu, Jianjiang Lu, Tian Y (2010) Technique of image retrieval based on multi-label image annotation. Multimed Inf Technol 2:10–13

    Google Scholar 

  8. Riad AM, Atwan A, Abdel-Ghany S (2008) Analysis of performance of mobile agents in distributed content based image retrieval. Comput Engineering & Systems 349–354

  9. Smith JR (1997) Integrated spatial and feature image analysis systems: retrieval, compression and analysis. Ph.D. Thesis, Graduate School of Arts and Sciences, Columbia University, February

  10. Stricker M, Orengo M (1995) Similarity of color images. Proc SPIE 2420:381–392

    Article  Google Scholar 

  11. Swain MJ, Ballard DH (1991) Color indexing. Int J Comput Vis 7(1):11–32

    Article  Google Scholar 

  12. Wang Z, Jia K, Liu P (2009) An effective web content-based image retrieval algorithm by using SIFT feature. Softw Eng 1:291–295

    Google Scholar 

  13. www.ctr.columbia.edu/advent

  14. wwwqbic.almaden,ibm.com/~qbic

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Daejin University, Pocheon, South Korea

    Yang-Hoon Kim

  2. Graduate School of Information, Yonsei University, Seoul, South Korea

    Hyuk-Jun Kwon

  3. Department of Business Administration, Daejin University, Pocheon, South Korea

    Jong-Gu Kang & Hangbae Chang

Authors
  1. Yang-Hoon Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hyuk-Jun Kwon
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jong-Gu Kang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hangbae Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hangbae Chang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kim, YH., Kwon, HJ., Kang, JG. et al. The study on content based multimedia data retrieval system. Multimed Tools Appl 57, 393–405 (2012). https://doi.org/10.1007/s11042-011-0758-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-011-0758-5

Keywords

Search

Navigation