Fix Links

3 files changed, 27 insertions, 25 deletions

diff --git a/report2/metadata.yaml b/report2/metadata.yaml
index 5500c63..5f9f737 100755
--- a/report2/metadata.yaml
+++ b/report2/metadata.yaml
@@ -2,7 +2,8 @@
 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
diff --git a/report2/paper.md b/report2/paper.md
index 20bd053..a961be0 100755
--- a/report2/paper.md
+++ b/report2/paper.md
@@ -180,6 +180,29 @@ 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 $top k$
@@ -211,28 +234,6 @@ 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
diff --git a/report2/template.latex b/report2/template.latex
index 49b4963..9917236 100644
--- a/report2/template.latex
+++ b/report2/template.latex
@@ -217,8 +217,8 @@ $if(author)$
         \IEEEauthorblockN{$author.name$}
         \IEEEauthorblockA{%
             $author.affiliation$ \\
-            $author.location$ \\
-            $author.email$}
+            $author.email$ \\
+            $author.link$}
         $sep$ \and
     $endfor$
 }