Home
Call For Papers
Submission
Author
Registration
Publications
About
Contact Us

  Large Scale Image Searching Using Two Dimensional Binary Trees Combined with Feature Selection  
  Authors : Radhakrishnan B; Anver Muhammed K.M
  Cite as:

 

The challenging task in image retrieval is to find similar images based on the distance between images. Content based image retrieval system is used to identify the similarity between multidimensional image feature vectors. If the database is large, searching the image linearly will be too slow. We have proposed the two dimensional binary tree approach for searching the images based on feature detectors and they have improve the retrieval efficiency significantly.

 

Published In : IJCSN Journal Volume 5, Issue 1

Date of Publication : February 2016

Pages : 142-145

Figures :01

Tables :

Publication Link : Large Scale Image Searching Using Two Dimensional Binary Trees Combined with Feature Selection

 

 

 

Radhakrishnan B : is working as Asst. Professor in Computer Science department. He has more than 14 years experience in teaching and has published papers on data mining and image processing. His research interests include image processing, data mining, and image mining

Anver Muhammed K.M : is working as System Administrator in Computer Science department. He has more than 10 years experience in System Administration and Implementation. His research interests include image processing and network security.

 

 

 

 

 

 

 

CBIR

Feature Detectors

Hessian Detector

SIFT

Solution of finding the similar image items requires solving the nearest neighbor problem. Two dimensional binary search trees are an efficient method for finding nearest neighbors. In this paper we have adapted the search trees to the problem of image retrieval and found the best parameters regarding minimizing the access time. Threshold value can be approximated by a logarithmic function of the number of feature vectors.

 

 

 

 

 

 

 

 

 

[1] Mohamed Aly. Online Learning for Parameter Selection in Large Scale Image Search. In CVPR Workshop OLCV, June 2010. [2] A. Andoni and P. Indyk. Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. Commun. ACM, 51(1):117122, 2008. [3] S. Arya, D.M. Mount, N.S. Netanyahu, R. Silverman, and A.Y. Wu. An optimal algorithm for approximate nearest neighbor searching. Journal of the ACM, 45:891923, 1998. [4] R. Baeza-Yates and B. Ribeiro-Neto. Modern Information Retrieval. ACM Press,1999. [5] Andrei Broder, Moses Charikar, and Michael Mizenmacher. Min-wise independent permutations. Journal of Computer and System Sciences, 60:630659, 2000. [6] Andrei Z. Broder, Steven C. Glassman, Mark S. Manasse, and Geoffrey Zweig. Syn- tactic clustering of the web. Computer Networks and ISDN Systems, 29:8 13, 1997. [7] A.Z. Broder. On the resemblance and containment of documents. In Proc. Compres- sion and Complexity of Sequences 1997, pages 2129, 1997. [8] J. Sivic and A. Zisserman. Video google: A text retrieval approach to object matching in videos. In ICCV, 2003. [9] M. Muja and D. Lowe. Fast approximate nearest neighbors with automatic algorithm configuration. In VISAPP, 2009.