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[Research] Efficient Nearest Neighbors Search in Distributed Manner

·1 min
project research
Table of Contents

Introduction #

This is my final year project associated with The Husky Lab under the supervison of Prof.James Cheng. In this project, I built an Image Retrieval System using LoSHa, a A general framework for scalable Locality Sensitive Hashing (LSH). This research project shows more salability and a 15% increase in speed when compared to OpenCV FLANN.

The details of the project can be found in this report or this slide. The source code will soon open-source.

Here I excerpt some of the main concepts of the project. What I aim to do is to look for the original piece of image within a directory full of images given part of the image that could have been altered and cropped.

Technology #

  • Husky & LoSHa
  • C/C++
  • OpenCV

Overview & Results #

There are three steps to complete the process:

  1. Create Image Binary
  2. Run nearest neighbor search (NNS)
  3. Match image

With the contrast and lightening altered duck.jpg, we can correctly find the original image within the directory.

Acknowledgements #

The image on Github are obtained from the internet under Creative Common CC0 licence and will remain as it is.