A Method for Near Duplicate Image Matching

Asst.Prof. Namrata D. Ghuse, Pallavi R. Kudal


The image can be modified using some transformation of original images that form close to duplicating images. An image is delineated by its size or length and the range of patches within the image varies with relevance the length. Similar patches are considered to be a similarity measure between two duplicate images. Image portrayal and Image similitude estimation are two noteworthy issues in the image coordinating. The proposed strategy extricates patches from is given an image and speaks to by factor length signature. The mark is additionally approved in a near duplicate image characteristic image recognition, which settles on a choice about whether two images are duplicates or not. The near-duplicate image recovery goals for recovering important images from an image database which are like question image.

Full Text:



Li Liu, Yue Lu, Senior Member, IEEE, and Ching Y. Suen, Fellow,


G.-H. Liu and J.-Y. Yang, Content-based image retrieval using color difference histogram, Pattern Recognit., vol. 46, no. 1, pp. 188198, 2013.

B. Wang, Z. Li, M. Li, and W.-Y. Ma, "Large-scale duplicate detection for web image search," , Jul. 2006, pp. 353-356.

David G. Lowe, Computer Science Department, University of British Columbia Vancouver, B.C., Canada

D.-Q. Zhang and S.-F. Chang, ”Detecting image near duplicate by stochastic attributed relational graph matching with learning,”,2004, pp. 877-884.

S.-H. Cha and S. N. Srihari, “On measuring the distance between histograms,” Pattern Recognit., vol. 35, no. 6, pp. 1355–1370, 2002.

X.Wan,A novel document similarity measure based on earthmovers distance, Inf. Sci., vol. 177, no. 18, pp. 37183730, 2007.

B. Wang, Z. Li, M. Li, and W.-Y. Ma, ”Large-scale duplicate detection for web image search,” , Jul. 2006, pp. 353356.

F. Zou et al., ”Nonnegative sparse coding induced hashing for image copy detection,” Neurocomputing, vol. 105, no. 1, pp. 81-89, 2013.

Y. Meng, E. Chang, and B. Li, ”Enhancing DPF for near-replica image recognition,” , Jun. 2003, pp. II416II-423.

S. Belongie, J. Malik, and J. Puzicha, Shape matching and object recognition using shape contexts, IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 4, pp. 509522, Apr. 2002.

J. Philbin ,O. Chum, M. Isard, J. Sivic, and A.Zisserman, Lost in quantization: Improving particular object retrieval in large scale image databases, Jun. 2008, pp. 18.

G. Meng, N. Zheng, Y. Zhang, and Y. Song, Document images retrieval based on multiple features combination, Sep. 2007, pp. 143147.

M. Ester, H. P. Kriegel, J. Sander, and X. Xu, A density-based algorithm for discovering clusters in large spatial databases with noise,1996, pp. 226231.

M. Heikkila, M. Pietikainen, and C. Schmid, Description of interest regions with local binary patterns, Pattern Recognit., vol. 42, no. 3,pp. 425436, 2009.

A. Vailaya, M.A.T. Figueiredo, A.K. Jain, H.J. Zhang, Image classification for content-based indexing, (2001) 117130.

S. Todorovic and N. Ahuja, Region-based hierarchical image matching, Int. J. Comput. Vis., vol. 78, no. 1, pp. 4766, 2008.

K. Mikolajczyk and C. Schmid, A performance evaluation on local descriptors, IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 10, pp. 16151630, Oct. 2005


  • There are currently no refbacks.


Copyright © IJETT, International Journal on Emerging Trends in Technology