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  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

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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.









Feature Detectors

Hessian Detector


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.










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