---
title: 'EE4-68 Pattern Recognition (2018-2019) CW2'
author:
  - name: Vasil Zlatanov (01120518), Nunzio Pucci (01113180)
    email: vz215@ic.ac.uk, np1915@ic.ac.uk
    link: 'Sources: < [git](https://git.skozl.com/e4-pattern/) - [tar](https://git.skozl.com/e4-pattern/snapshot/vz215_np1915-master.tar.gz) - [zip](https://git.skozl.com/e4-pattern/snapshot/vz215_np1915-master.zip) - [man](https://git.skozl.com/e4-pattern/about/) >' 
numbersections: yes
lang: en
babel-lang: english
nocite: |
          @deepreid, @sklearn

abstract: |
  This report analyses distance metrics learning techniques with regards to 
  identification accuracy for the dataset CUHK03. The baseline method used for 
  identification is Nearest Neighbours based on Euclidean distance. 
  The improved approach evaluated utilises Jaccardian metrics to rearrange the NN
  ranklist based on reciprocal neighbours. While this approach is more complex and introduces new hyperparameters, 
  significant accuracy improvements are observed -
  approximately 10% higher Top-1 identification, and good improvements for Top-$N$ accuracy with low $N$.
...