1 files changed, 19 insertions, 25 deletions
diff --git a/report2/paper.md b/report2/paper.md
index 0ceeedd..e4c3ec0 100755
--- a/report2/paper.md
+++ b/report2/paper.md
@@ -1,17 +1,13 @@
-# Summary
-In this report we analysed how distance metrics learning affects classification
-accuracy for the dataset CUHK03. The baseline method used for classification is
-Nearest Neighbors based on Euclidean distance. The improved approach we propose 
-mixes Jaccardian and Mahalanobis metrics to obtain a ranklist that takes into 
-account also the reciprocal neighbors. This approach is computationally more 
-complex, since the matrices representing distances are effectively calculated 
-twice. However it is possible to observe a significant accuracy improvement of 
-around 10% for the $@rank1$ case. Accuracy improves overall, especially for
-$@rankn$ cases with low n.
-
 # Formulation of the Addresssed Machine Learning Problem
 
-## CUHK03
+## Probelm Definition
+
+The problem to solve is to create a ranklist for each image of the query set
+by finding the nearest neighbor(s) within a gallery set. However gallery images
+with the same label and taken from the same camera as the query image should
+not be considered when forming the ranklist.
+
+## Dataset - CUHK03
 
 The dataset CUHK03 contains 14096 pictures of people captured from two 
 different cameras. The feature vectors used come from passing the
@@ -23,13 +19,6 @@ on a training set (train_idx, adequately split between test, train and
 validation keeping the same number of identities). This prevents overfitting 
 the algorithm to the specific data associated with query_idx and gallery_idx.
 
-## Probelm to solve
-
-The problem to solve is to create a ranklist for each image of the query set
-by finding the nearest neighbor(s) within a gallery set. However gallery images
-with the same label and taken from the same camera as the query image should
-not be considered when forming the ranklist.
-
 ## Nearest Neighbor ranklist
 
 Nearest Neighbor aims to find the gallery image whose feature are the closest to
@@ -46,7 +35,7 @@ EXPLAIN KNN BRIEFLY
 \begin{figure}
 \begin{center}
 \includegraphics[width=20em]{fig/baseline.pdf}
-\caption{Recognition accuracy of baseline Nearest Neighbor @rank k}
+\caption{Top K Accuracy for Nearest Neighbour classification}
 \label{fig:baselineacc}
 \end{center}
 \end{figure}
@@ -54,26 +43,31 @@ EXPLAIN KNN BRIEFLY
 \begin{figure}
 \begin{center}
 \includegraphics[width=22em]{fig/eucranklist.png}
-\caption{Ranklist @rank10 generated for 5 query images}
+\caption{Top 10 ranklist for 5 probes}
 \label{fig:eucrank}
 \end{center}
 \end{figure}
 
-
 # Suggested Improvement
 
 \begin{figure}
 \begin{center}
 \includegraphics[width=24em]{fig/ranklist.png}
-\caption{Ranklist (improved method) @rank10 generated for 5 query images}
+\caption{Top 10 ranklist (improved method) 5 probes}
 \label{fig:ranklist2}
 \end{center}
 \end{figure}
 
+
+TODO:
+~~
+s/kNN/NN/
+~~
+
 \begin{figure}
 \begin{center}
 \includegraphics[width=20em]{fig/comparison.pdf}
-\caption{Comparison of recognition accuracy @rank k}
+\caption{Top K Accurarcy}
 \label{fig:baselineacc}
 \end{center}
 \end{figure}
@@ -81,7 +75,7 @@ EXPLAIN KNN BRIEFLY
 \begin{figure}
 \begin{center}
 \includegraphics[width=17em]{fig/pqvals.pdf}
-\caption{Recognition accuracy varying K1 and K2}
+\caption{Top 1 Accuracy when k1 and k2}
 \label{fig:pqvals}
 \end{center}
 \end{figure}