Add figures

1 files changed, 25 insertions, 43 deletions

diff --git a/report2/paper.md b/report2/paper.md
index 75989a1..7099df8 100755
--- a/report2/paper.md
+++ b/report2/paper.md
@@ -108,6 +108,24 @@ We find that for the query and gallery set clustering does not seem to improve i
 
 # Suggested Improvement
 
+## Comment on Mahalnobis Distance as a metric
+
+We were not able to achieve significant improvements using mahalanobis for 
+original distance ranking compared to square euclidiaen metrics. Results can 
+be observed using the `-m|--mahalanobis` when running evalution with the 
+repository complimenting this paper.
+
+COMMENT ON VARIANCE AND MAHALANOBIS RESULTS
+
+\begin{figure}
+\begin{center}
+\includegraphics[width=12em]{fig/cdist.pdf}
+\includegraphics[width=12em]{fig/train_subspace.pdf}
+\caption{Left:first two features of gallery(o) and query(x) data for 3 labels; Right:First two features of train data for three labels}
+\label{fig:subspace}
+\end{center}
+\end{figure}
+
 ## k-reciprocal Reranking Formulation
 
 The approach addressed to improve the identification performance is based on
@@ -180,7 +198,7 @@ has improved for the fifth query. The mAP improves from 47% to 61.7%.
 
 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%.
+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}
@@ -191,11 +209,15 @@ 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)}
+\caption{Identification accuracy varying K1 and K2 (gallery-query left, train right) KL=0.3}
 \label{fig:pqvals}
 \end{center}
 \end{figure}
@@ -204,55 +226,15 @@ The difference between the $top k$ accuracies of the two methods gets smaller as
 \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)}
+\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}
 
-# Comment on Mahalnobis Distance as a metric
-
-We were not able to achieve significant improvements using mahalanobis for 
-original distance ranking compared to square euclidiaen metrics. Results can 
-be observed using the `-m|--mahalanobis` when running evalution with the 
-repository complimenting this paper.
-
-COMMENT ON VARIANCE AND MAHALANOBIS RESULTS
-
 # Conclusion
 
 # References
 
 # Appendix
 
-\begin{figure}
-\begin{center}
-\includegraphics[width=17em]{fig/cdist.pdf}
-\caption{First two features of gallery(o) and query(x) feature data}
-\label{fig:subspace}
-\end{center}
-\end{figure}
-
-\begin{figure}
-\begin{center}
-\includegraphics[width=17em]{fig/clusteracc.pdf}
-\caption{Top k identification accuracy for cluster count}
-\label{fig:clustk}
-\end{center}
-\end{figure}
-
-\begin{figure}
-\begin{center}
-\includegraphics[width=17em]{fig/jaccard.pdf}
-\caption{Explained Jaccard}
-\label{fig:jaccard}
-\end{center}
-\end{figure}
-
-\begin{figure}
-\begin{center}
-\includegraphics[width=17em]{fig/mahalanobis.pdf}
-\caption{Explained Mahalanobis}
-\label{fig:mahalanobis}
-\end{center}
-\end{figure}