aboutsummaryrefslogtreecommitdiff
path: root/report2/paper.md
diff options
context:
space:
mode:
Diffstat (limited to 'report2/paper.md')
-rwxr-xr-xreport2/paper.md17
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}