1 files changed, 15 insertions, 2 deletions
diff --git a/report2/paper.md b/report2/paper.md
index 8e76142..75989a1 100755
--- a/report2/paper.md
+++ b/report2/paper.md
@@ -50,7 +50,7 @@ be used as an alternative to euclidiean distance.
 To evaluate improvements brought by alternative distance learning metrics a baseline 
 is established through nearest neighbour identification as previously described. 
 Identification accuracies at top1, top5 and top10 are respectively 47%, 67% and 75%
-(figure \ref{fig:baselineacc}). The mAP for a ranklist of size 10 is 33.3%.
+(figure \ref{fig:baselineacc}). The mAP is 47%.
 
 \begin{figure}
 \begin{center}
@@ -160,7 +160,15 @@ This is done through a simple **GRADIENT DESCENT** algorithm followed by exhaust
 $k_{1_{opt}}$ 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: $k_{1_{opt}}=9, k_{2_{opt}}=3, 0.1\leq\lambda_{opt}\leq 0.3$.
 
-## k-reciprocal Reranking Formulation
+## 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.
+
+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%.
 
 \begin{figure}
 \begin{center}
@@ -170,6 +178,11 @@ in steps of 0.1. The results obtained through this approach suggest: $k_{1_{opt}
 \end{center}
 \end{figure}
 
+Figure \ref{fig:compare} shows a comparison between $top k$ identification accuracies
+obtained with the two methods. It is noticeable that the k-reciprocal reranking method significantly
+improves the results even for top1, boosting identification accuracy from 47% to 56.5%.
+The difference between the $top k$ accuracies of the two methods gets smaller as we increase k.
+
 \begin{figure}
 \begin{center}
 \includegraphics[width=20em]{fig/comparison.pdf}