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diff --git a/report/metadata.yaml b/report/metadata.yaml new file mode 100755 index 0000000..5f9f737 --- /dev/null +++ b/report/metadata.yaml @@ -0,0 +1,21 @@ +--- +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) >' +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 Eucdidian based Nearest Neighbors 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 introduced new hyperparameter, significant accuracy improvements are observed - + approximately 10% increased Top-1 identifications, and good improvements for Top-$N$ accuracy with low $N$. +... + |