1 files changed, 58 insertions, 53 deletions
diff --git a/report2/paper.md b/report2/paper.md
index 4ccf5ca..7cb28e3 100755
--- a/report2/paper.md
+++ b/report2/paper.md
@@ -4,12 +4,12 @@
 ## Probelm Definition
 
 The person re-identification problem presented in this paper requires mtatching 
-pedestrian images from disjoint camera's by pedestrian detectors. This problem is 
+pedestrian images from disjoint cameras by pedestrian detectors. This problem is 
 challenging, as identities captured in photsos are subject to various lighting, pose, 
 blur, background and oclusion from various camera views. This report considers 
 features extracted from the CUHK03 dataset, following a 50 layer Residual network 
 (Resnet50). This paper considers distance metrics techniques which can be used to 
-perform person re-identification across **disjoint* cameras, using these features.
+perform person re-identification across *disjoint* cameras, using these features.
 
 ## Dataset - CUHK03  Summary
 
@@ -51,7 +51,9 @@ be used as an alternative to euclidiean distance.
 # Baseline Evaluation
 
 To evaluate improvements brought by alternative distance learning metrics a baseline 
-is established as trough nearest neighbour identification as previously described.
+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%.
 
 \begin{figure}
 \begin{center}
@@ -61,6 +63,10 @@ is established as trough nearest neighbour identification as previously describe
 \end{center}
 \end{figure}
 
+Figure \ref{fig:eucrank} shows the ranklist generated through baseline NN for 
+5 query images(black). Correct identification is shown in green and incorrect 
+identification is shown in red.
+
 \begin{figure}
 \begin{center}
 \includegraphics[width=22em]{fig/eucranklist.png}
@@ -69,11 +75,32 @@ is established as trough nearest neighbour identification as previously describe
 \end{center}
 \end{figure}
 
-
-# Suggested Improvement
+Normalization  of the feature vectors does not improve accuracy results of the 
+baseline as it can be seen in figure \ref{fig:baselineacc}. ###EXPLAIN WHY
 
 ## kMean Clustering
 
+An addition considered for the baseline is *kMeans clustering*. In theory this method 
+allows to reduce computational complexity of the baseline NN by forming clusters and
+performing a comparison between query image and clusters centers. The elements
+associated with the closest cluster center are then considered to perform NN and
+classify the query image. 
+
+This method did not bring any major improvement to the baseline, as it can be seen from
+figure \ref{fig:baselineacc}. It is noticeable how the number of clusters affects
+performance, showing better identification accuracy for a number of clusters away from
+the local minimum achieved at 60 clusters (figure \ref{fig:kmeans}). ###EXPLAIN WHY
+
+
+\begin{figure}
+\begin{center}
+\includegraphics[width=17em]{fig/kmeanacc.pdf}
+\caption{Top 1 Identification accuracy varying kmeans cluster size}
+\label{fig:kmeans}
+\end{center}
+\end{figure}
+
+# Suggested Improvement
 
 ## k-reciprocal Reranking
 
@@ -89,20 +116,43 @@ is established as trough nearest neighbour identification as previously describe
 \begin{center}
 \includegraphics[width=20em]{fig/comparison.pdf}
 \caption{Comparison of recognition accuracy @rank k (KL=0.3,K1=9,K2=3)}
-\label{fig:baselineacc}
+\label{fig:compare}
 \end{center}
 \end{figure}
 
 \begin{figure}
 \begin{center}
-\includegraphics[width=17em]{fig/pqvals.pdf}
-\caption{Identification accuracy varying K1 and K2}
+\includegraphics[width=12em]{fig/pqvals.pdf}
+\includegraphics[width=12em]{fig/trainpqvals.pdf}
+\caption{Identification accuracy varying K1 and K2 (gallery-query left, train right)}
 \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)}
+\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.
+
+# 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}
@@ -127,54 +177,9 @@ is established as trough nearest neighbour identification as previously describe
 
 \begin{figure}
 \begin{center}
-\includegraphics[width=17em]{fig/kmeanacc.pdf}
-\caption{Top 1 Identification accuracy varying kmeans cluster size}
-\label{fig:kmeans}
-\end{center}
-\end{figure}
-
-\begin{figure}
-\begin{center}
-\includegraphics[width=17em]{fig/lambda_acc.pdf}
-\caption{Top 1 Identification Accuracy with Rerank (varying lambda)}
-\label{fig:lambdagal}
-\end{center}
-\end{figure}
-
-\begin{figure}
-\begin{center}
-\includegraphics[width=17em]{fig/lambda_acc_tr.pdf}
-\caption{Top 1 Identification Accuracy with Rerank (varying lambda on train data)}
-\label{fig:lambdatr}
-\end{center}
-\end{figure}
-
-\begin{figure}
-\begin{center}
 \includegraphics[width=17em]{fig/mahalanobis.pdf}
 \caption{Explained Mahalanobis}
 \label{fig:mahalanobis}
 \end{center}
 \end{figure}
 
-\begin{figure}
-\begin{center}
-\includegraphics[width=17em]{fig/trainpqvals.pdf}
-\caption{Identification accuracy varying K1 and K2(train)}
-\label{fig:pqtrain}
-\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.
-
-# Conclusion
-
-# References
-
-# Appendix
-