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An Implementation of Effective Logo Matching and Detection using Multiple Descriptors to Enhance the Resolution

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Poornaiah Billa, Ashok Kumar Balijepalli, I. RamaKoteswara Rao

Poornaiah Billa, Ashok Kumar Balijepalli and RamaKoteswara I Rao. An Implementation of Effective Logo Matching and Detection using Multiple Descriptors to Enhance the Resolution. International Journal of Computer Applications 161(5):24-27, March 2017. BibTeX

	author = {Poornaiah Billa and Ashok Kumar Balijepalli and I. RamaKoteswara Rao},
	title = {An Implementation of Effective Logo Matching and Detection using Multiple Descriptors to Enhance the Resolution},
	journal = {International Journal of Computer Applications},
	issue_date = {March 2017},
	volume = {161},
	number = {5},
	month = {Mar},
	year = {2017},
	issn = {0975-8887},
	pages = {24-27},
	numpages = {4},
	url = {},
	doi = {10.5120/ijca2017913200},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


In current trends the logos are playing a vital role in industrial and all commercial applications. Fundamentally the logo is defined as it’s a graphic entity which contains colors textures, shapes and text etc., which is organized in some special visible format. But unfortunately it is very difficult thing to save their brand logos from duplicates. In practical world there are several systems available for logo reorganization and detection with different kinds of requirements. In some partial occlusions it should be robust to transfer the large range of photometric and geometric features of a logo which they are not captured in isolation. Two dimensional global descriptors are used for logo matching and reorganization. The concept of Shape descriptors based on Shape context and the global descriptors are based on the logo contours. There is an algorithm which is implemented for logo detection is based on partial spatial context and spatial spectral saliency (SSS). The SSS is able to keep away from the confusion effect of background and also speed up the process of logo detection. All such methods are useful only when the logo is visible completely without noise and not subjected to change. These types of methods are not suitable for practical images where insufficient resolution is the drawback of these methods. To overcome these drawbacks we proposed a multiple descriptors method along with context dependent similarity concept. The multiple descriptors are scale invariant feature transform (SIFT), Speeded up robust feature (SURF), histogram oriented gradient (HOG) and Gradient location and orientation histogram (GLOH). By using this method we assure high resolution and great accuracy.


  1. Chinmoy Biswas,Joydeep Mukherjee “Logo Recognition Technique using Sift Descriptor, Surf Descriptor and Hog Descriptor” International Journal of Computer Applications (0975 - 8887) Volume 117 No. 22, May 2015.
  2. Ch.Divya Susmitha, L.Padmalatha “Context Dependent Logo Detection and Recognition based on Context Dependent Similarity” Kernel International Journal of Computer Applications (0975 - 8887) Volume 106 - No.11, November 2014.
  3. Apostolos p. Psyllos, christos-nikolaos e. Anagnostopoulos “vehicle logo recognition using a sift-based Enhanced matching scheme” IEEE transactions on intelligent transportation systems, vol. 11, no. 2, june 2010.
  4. Shu-Kuo Sun And Zen Chen “Robust Logo Recognition For Mobile Phone Applications”Journal Of Information Science And Engineering 27, 545-559 (2011).
  5. Ke gao , shouxun lin1, yongdong zhang , sheng tang, dongming zhang “logo detection based on spatial-spectral saliency and Partial spatial context” national basic research Program of china (973 program, 2007cb311100),National high technology and research development Program of china (863 program, 2007aa01z416).
  6. B.Suganya and A.C.Santha Sheela“Finding Fake Logo Using CDS Logo Detection And Recognition Algorithm” International Journal of Scientific & Engineering Research, Volume 5, Issue 3, March-2014.
  7. Wenju Li and Ling Li, “A Novel Approach for Vehicle-logo Location Based on Edge Detection and Morphological Filter, 2nd International Symposium on Electronic Commerce and Security, Nanchang, vol: 1, pp. 343-345, 2009.
  8. Nabeel Younus Khan,Brendan McCane,Geoff Wyvill “SIFT and SURF Performance Evaluation Against Various Image Deformations on Benchmark Dataset, International Conference on Digital Image Computing: Techniques and Applications,2011 ,PP.501-506.
  9. Syed Yasser Arafat, Syed Afaq Husain, Iftikhar Azim Niaz and Muhammad Saleem, “Logo Detection and Recognition in Video Stream”, 5th International Conference on Digital Information Management, Thunder Bay, Canada, pp. 163 - 168, 2010.
  10. Ma Y F, Zhang H J, “Contrast-based Image Attention Analysis By Using Fuzzy Growing. Proceedings of the 11th ACM International Conference on Multimedia (MM2003). Berkeley, CA, USA: ACM, pp.374 - 381, 2003.
  11. Xiaodi Hou, Liqing Zhang, "Saliency Detection: A Spectral Residual Approach," Computer Vision and Pattern Recognition (CVPR), 2007.
  12. Herve Jegou, Matthijs Douze, and Cordelia Schmid, “Hamming Embedding and Weak Geometric Consistency For Large Scale Image Search,”ECCV, 2008.
  13. Sivic. J, Zisserman. A, “Video Google: A text retrieval approach to object matching in videos.” In: ICCV. pp. 1470-1477.2003.


Logo Matching and Recognition, Context Dependent Similarity, Scale Invariant Feature Transform (SIFT), Speeded Up Robust Feature (SURF), Histogram Oriented Gradient (HOG) and Gradient Location and Orientation Histogram (GLOH).