diff options
Diffstat (limited to 'report')
| -rwxr-xr-x | report/metadata.yaml | 6 | ||||
| -rwxr-xr-x | report/paper.md | 52 | ||||
| -rw-r--r-- | report/template.latex | 4 |
3 files changed, 32 insertions, 30 deletions
diff --git a/report/metadata.yaml b/report/metadata.yaml index 20375f9..5f9f737 100755 --- a/report/metadata.yaml +++ b/report/metadata.yaml @@ -2,10 +2,14 @@ title: 'EE4-68 Pattern Recognition (2018-2019) CW2' author: - name: Vasil Zlatanov (01120518), Nunzio Pucci (01113180) - location: vz215@ic.ac.uk, np1915@ic.ac.uk + 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 diff --git a/report/paper.md b/report/paper.md index 6358445..a961be0 100755 --- a/report/paper.md +++ b/report/paper.md @@ -130,7 +130,7 @@ repository complimenting this paper. The approach addressed to improve the identification performance is based on k-reciprocal reranking. The following section summarizes the idea behind -the method illustrated in **REFERENCE PAPER**. +the method illustrated in reference @rerank-paper. We define $N(p,k)$ as the top k elements of the ranklist generated through NN, where p is a query image. The k reciprocal ranklist, $R(p,k)$ is defined as the @@ -180,12 +180,35 @@ This is done through a simple multi-direction search algorithm followed by exhau $k_{1_{opt}}$ and $k_{2_{opt}}$ for eleven values of $\lambda$ from zero(only Jaccard distance) to one(only original distance) in steps of 0.1. The results obtained through this approach suggest: $k_{1_{opt}}=9, k_{2_{opt}}=3, 0.1\leq\lambda_{opt}\leq 0.3$. +It is possible to verify that the optimization of $k_{1_{opt}}$, $k_{2_{opt}}$ and $\lambda$ +has been successful. Figures \ref{fig:pqvals} and \ref{fig:lambda} show that the optimal values obtained from +training are close to the ones for the local maximum of gallery and query. + +\begin{figure} +\begin{center} +\includegraphics[width=12em]{fig/pqvals.pdf} +\includegraphics[width=12em]{fig/trainpqvals.pdf} +\caption{Identification accuracy varying K1 and K2 (gallery-query left, train right) KL=0.3} +\label{fig:pqvals} +\end{center} +\end{figure} + +\begin{figure} +\begin{center} +\includegraphics[width=12em]{fig/lambda_acc.pdf} +\includegraphics[width=12em]{fig/lambda_acc_tr.pdf} +\caption{Top 1 Identification Accuracy with Rerank varying lambda(gallery-query left, train right) K1=9, K2=3} +\label{fig:lambda} +\end{center} +\end{figure} + + ## k-reciprocal Reranking Evaluation Reranking achieves better results than the other baseline methods analyzed both as $top k$ accuracy and mean average precision. It is also necessary to estimate how precise the ranklist generated is. -For this reason an additional method of evaluation is introduced: mAP. +For this reason an additional method of evaluation is introduced: mAP. See reference @mAP. It is possible to see in figure \ref{fig:ranklist2} how the ranklist generated for the same five queries of figure \ref{fig:eucrank} has improved for the fifth query. The mAP improves from 47% to 61.7%. @@ -211,32 +234,7 @@ The difference between the $top k$ accuracies of the two methods gets smaller as \end{center} \end{figure} -It is possible to verify that the optimization of $k_{1_{opt}}$, $k_{2_{opt}}$ and $\lambda$ -has been successful. Figures \ref{fig:pqvals} and \ref{fig:lambda} show that the optimal values obtained from -training are close to the ones for the local maximum of gallery and query. - -\begin{figure} -\begin{center} -\includegraphics[width=12em]{fig/pqvals.pdf} -\includegraphics[width=12em]{fig/trainpqvals.pdf} -\caption{Identification accuracy varying K1 and K2 (gallery-query left, train right) KL=0.3} -\label{fig:pqvals} -\end{center} -\end{figure} - -\begin{figure} -\begin{center} -\includegraphics[width=12em]{fig/lambda_acc.pdf} -\includegraphics[width=12em]{fig/lambda_acc_tr.pdf} -\caption{Top 1 Identification Accuracy with Rerank varying lambda(gallery-query left, train right) K1=9, K2=3} -\label{fig:lambda} -\end{center} -\end{figure} - # Conclusion # References -# Appendix - - diff --git a/report/template.latex b/report/template.latex index 49b4963..9917236 100644 --- a/report/template.latex +++ b/report/template.latex @@ -217,8 +217,8 @@ $if(author)$ \IEEEauthorblockN{$author.name$} \IEEEauthorblockA{% $author.affiliation$ \\ - $author.location$ \\ - $author.email$} + $author.email$ \\ + $author.link$} $sep$ \and $endfor$ } |