1 files changed, 25 insertions, 27 deletions
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
-
-