Advertisement

Integrating color and spatial feature for content-based image retrieval

  • Cao Kui
  • Feng Yu-cai
Article
  • 37 Downloads

Abstract

In this paper, we present a novel and efficient scheme for extracting, indexing and retrieving color images. Our motivation was to reduce the space overhead of partition-based approaches taking advantage of the fact that only a relatively low number of distinct values of a particular visual feature is present in most images. To extract color feature and build indices into our image database we take into consideration factors such as human color perception and perceptual range, and the image is partitioned into a set of regions by using a simple classifying scheme. The compact color feature vector and the spatial color histogram, which are extracted from the seqmented image region, are used for representing the color and spatial information in the image. We have also developed the region-based distance measures to compare the similarity of two images. Extensive tests on a large image collection were conducted to demonstrate the effectiveness of the proposed approach.

Key words

color distribution spatial color histogram region-based image representation and retrieval similarity matching integrating of single features 

CLC number

TP 311.13 

References

  1. [1]
    Swain M, Ballard D. Color Indexing.International Journal of Computer Vision, 1991,7(1):11–32.CrossRefGoogle Scholar
  2. [2]
    Stricker M A, Orengo M. Similarity of Color Images.Proc of SPIE Storage and Retrieval for Image and Video Databases, 1995,2420:381–392.Google Scholar
  3. [3]
    Paschos G. Fast Colour Texture Recognition Using Chromaticity Moments.Pattern Recognition Letters, 2000,21:837–841.CrossRefGoogle Scholar
  4. [4]
    Huang J, Kumar S R. Image Indexing Using Color Correlograms.IEEE Computer Vision and Pattern Recognition Conference, Puerto Pico, 1997, 762–768.Google Scholar
  5. [5]
    Appas A R, Darwish A M. Image Indexing Using Composite Regional Color Channels Features.Proc of SPIE Storage and Retrieval for Image and Video Database VII, San Jose, CA, 1999,3656:492–500.Google Scholar
  6. [6]
    Sciasio E D, Mingolla G, Mongiello M. Content-Based Image Retrieval Over the Web Using Query by Sketch and Relevance Feedback.Lecture Notes in Computer Science, 1999,1614:123–130.CrossRefGoogle Scholar
  7. [7]
    Stehling R O, Nascimento M A, Falcao A X. On Shape’ of Colors for Content-Based Image Retrieval. Procof the ACM Intl Workshop on Multimedia Information Retrieval, Los Angeles, 2000, 171–174.Google Scholar
  8. [8]
    Li J, Wang J Z, Wiederhold G. Irm: Integrated Region Matching for Image Retrieval.Proc Of 8th ACM Intl Conference on Multimedia, Los Angeles, 2000, 147–156.Google Scholar
  9. [9]
    Stehling R O, Nascimento M A, Falcao A X. An Adaptive and Efficient Clustering-Based Approach for Content-Based Retrieval in Image Database.Proc Of Intl Database Engineering & Applications Symposium, Grenoble, France, 2001, 356–365.Google Scholar
  10. [10]
    Wyszecki G, Stiles W S. Color Science:Concepts and Methods, Quantitative Data and Formulae. New York: Wiley, 1982.Google Scholar
  11. [11]
    Carson C, Ogle V E. Storage and Retrieval of Feature Data for a Very Large Online Image Collection.IEEE Bulletin of IEEE Computer Society Technical Committee on Data Enginnering, 1996,19:19–25.Google Scholar
  12. [12]
    Kulkarni S, Verma B,et al. Content Based Image Retrieval Using a Neuro-Fuzzy Technique.IEEE Int. Joint Conf on Neural Networks, Washington DC, 1999, 846–850.Google Scholar
  13. [13]
    Androutsos D, Plataniotis K N, Venetsanopoulos A N. A Novel Vector-Based Approach to Color Image Retrieval Using a Vector Angular-Based Distance Measure.Computer Vision and Image Understanding, 1999,75(1/2):46–58.CrossRefGoogle Scholar

Copyright information

© Springer 2002

Authors and Affiliations

  • Cao Kui
    • 1
  • Feng Yu-cai
    • 1
  1. 1.School of Computer Science & TechnologyHuazhong University of Science and TechnologyWuhan, HubeiChina

Personalised recommendations