Analysis of Color Moment as a Low Level Feature in Improvement of Content Based Image Retrieval

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 221)


In the recent past, the rapid intensification of the Internet has significantly enhanced the quantity of image collections accessible owing to the simplicity with which images are being formed or stored. CBIR (Content Based Image Retrieval) phenomenon is therefore highly encouraged by this requirement of unbeaten and proficient exploration of the large image databases. Consequently, the low level feature extraction of the visual contents of an image and their analysis is very significant in terms of CBIR. These low level features can be colour, texture and shape features. As Colour based image retrieval procedure is the trendiest of all these feature extraction algorithms, hence in this paper the color moments of the Hue, Saturation, and Value (HSV) component images in HSV color space are used as feature extraction algorithm. After the successful calculation of features for extraction, similarity computation is done using Euclidean Distance in between the test image and object images and finally the image retrieval is done. Analysis of this paper shows that the training time required for individual image, as well as, all the images in the database is very small which provides instantaneous retrieval. The estimation of the proposed approach is conceded out using the standard precision, recall and f-score measures, and the experimental results demonstrate that the proposed method has higher accuracy and retrieval rate than the conventional methods.


Image retrieval Feature extraction algorithm HSV color space Color moments Euclidean distance Precision Recall F-score 


  1. 1.
    Gevers T, Smeulders AWM (1999) Content-based image retrieval by viewpoint-invariant image indexing. Image Vis Comput 17(7):475–488CrossRefGoogle Scholar
  2. 2.
    Smeulders AWM, Worring M, Satini S, Gupta A, Jain R (2000) Content—based image retrieval at the end of the early years. IEEE Trans Pattern Anal Mach Intell 22(12):1349–1380Google Scholar
  3. 3.
    Zhu L, Zhang A, Rao A, Srihari R (2000) Keyblock: an approach for content-based image retrieval. In: Proceedings of ACM multimedia conference, 157–166Google Scholar
  4. 4.
    Gupta A, Jain R (1997) Visual information retrieval. Commun ACM 40(5):71–79Google Scholar
  5. 5.
    Ma JQ (2009) Content Based Image Retrieval with HSV color space and texture feature. International conference on web information systems and mining (WISM ’09), 7–8 November 2009, Shanghai, ChinaGoogle Scholar
  6. 6.
    Tamura H, Yokoya N (1984) Image database systems: a survey. Pattern Recogn 17(1):29–43CrossRefGoogle Scholar
  7. 7.
    Datta R, Joshi D, Jiali, Wang JZ (2008) Image retrieval: ideas, influences and trends of the new age. ACM Trans Comput Surv 40(2)Google Scholar
  8. 8.
    Jeong S (2001) Histogram based colour image retrieval Psycs221/EE362 Project report, Mar 2001Google Scholar
  9. 9.
    Wang JZ (2001) Integrated region-based image retrieval. Kluwer Academic Publishers, BostonMATHCrossRefGoogle Scholar
  10. 10.
    Brunelli R, Mich O (2001) Histograms analysis for image retrieval. Pattern Recogn 34(8):1625–1637MATHCrossRefGoogle Scholar
  11. 11.
    Del Bimbo A, Mugnaini M, Pala P, Turco Picasso F (1997) Visual querying by color perceptive regions. In: Proceedings of the 2nd international conference on visual information systems, San Diego, pp 125–131Google Scholar
  12. 12.
    Huang J, Kumar SR, Mitra M, Zhu WJ, Zabih R (1997) Image indexing using color correlograms. IEEE International Conference Computer Vision and Pattern Recognition, San Juan, Puerto Rico, pp 762–768Google Scholar
  13. 13.
    Huang Z et al (2010) CBIR using color moment and gabor texture feature. In: Proceedings of 9th international conference on machine learning and cybernetics in Qingdao, 11–14Google Scholar
  14. 14.
    Shih FT, Mitchell OR (1992) A mathematical morphology approach to Euclidean distance transformation. IEEE Trans Image Process 1(2):197–204Google Scholar

Copyright information

© Springer India 2013

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringNational Institute of TechnologyTripura (w)India

Personalised recommendations