An Efficient Image Retrieval System Using Surf Feature Extraction and Visual Word Grouping Technique

Authors

  • S. Bhuvana  Computer Science and Engineering, Sri Krishna College of Technology, Coimbatore, Tamil Nadu , India
  • F. Ragini Felicia Suruti  Computer Science and Engineering, Sri Krishna College of Technology, Coimbatore, Tamil Nadu , India
  • R. Shariene Fathima  Computer Science and Engineering, Sri Krishna College of Technology, Coimbatore, Tamil Nadu , India
  • P. Vincy Roshalin  Computer Science and Engineering, Sri Krishna College of Technology, Coimbatore, Tamil Nadu , India
  • M. Radhey  Computer Science and Engineering, Sri Krishna College of Technology, Coimbatore, Tamil Nadu , India

Keywords:

Image Retrieval, Bag-Of-Visual Words, Spatial Constraint, Salient Area Detection, And Mean-Shift Clustering.

Abstract

Content Based Image Retrieval is widely used to find the location of images for many application scenarios. It is used to tag the images using large geo-tagged image set. In recent years, images are tagged based on their locations and geo-tagged images consumes more memory space. Therefore, the performance of the image location estimation can be improved by using visual word groups. The mean shift clustering algorithm and a position descriptor have been used to generate visual word groups. A fast indexing structure is build using document builder interface. Thus the drawbacks in the existing system have been over-come in the proposed system. The proposed system involves Speeded-Up Robust Transform (SURF) which is a feature detection and descriptor method which is used for object recognition. The modules include Feature extraction which is used to identify the interest points within the image, Indexing which builds an inverted file structure to reduce the image size, Image retrieval which compares the input image with the query image to give the resultant output and Re-Ranking which categorizes the top ranked images.

References

  1. S. Zhang, Q. Tian, G. Hua, Q. Huang, and S. Li, “Descriptive visual words and visual phrases for image applications,” in Proc. 17th ACM MM, 2009, pp. 75–84.
  2. Y. Li, D. J. Crandall, and D. P. Huttenlocher, “Landmark classification in large-scale image collections,” in Proc. IEEE 12th ICCV, Sep. /Oct. 2009, pp. 1957–1964.
  3. G. Cheng et al., “Object detection in remote sensing imagery using a discriminatively trained mixture model,” ISPRS J. Photogram. Remote Sens., vol. 85, pp. 32–43, Nov. 2013.
  4. J. Li, X. Qian, Y. Y. Tang, L. Yang, and T. Mei, “GPS estimation for places of interest from social users’ uploaded photos,” IEEE Trans. Multimedia, vol. 15, no. 8, pp. 2058– 2071, Dec. 2013.
  5. C. Hauff and G.-J. Houben, “Placing images on the world map: A microblog-based enrichment approach,” in Proc. 35th ACM SIGIR, 2012, pp. 691–700.
  6. H. Liu, T. Mei, J. Luo, H. Li, and S. Li, “Finding perfect rendezvous on the go Accurate mobile visual localization and its applications to routing,” in Proc. 20th ACM Int. Conf. Multimedia, 2012, pp. 9–18.
  7. E. Gavves, C. G. M. Snoek, and A. W. M. Smeulders, “Visual synonyms for landmark image retrieval,” Comput. Vis. Image Understand., vol. 116, no. 2, pp. 238– 249, 2012.
  8. W. Zhou, Y. Lu, H. Li, Y. Song, and Q. Tian, “Spatial coding for large scale partial-duplicate Web image search,” in Proc. ACM Int. Conf. Multimedia, 2010, pp. 511–520.

Downloads

Published

2017-04-30

Issue

Section

Research Articles

How to Cite

[1]
S. Bhuvana, F. Ragini Felicia Suruti, R. Shariene Fathima, P. Vincy Roshalin, M. Radhey, " An Efficient Image Retrieval System Using Surf Feature Extraction and Visual Word Grouping Technique, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 2, pp.488-494, March-April-2017.