From ed44a6f432cf9e1051edd58e146a54124345adcd Mon Sep 17 00:00:00 2001
From: nunzip <np.scarh@gmail.com>
Date: Mon, 10 Dec 2018 17:29:04 +0000
Subject: Insert all graphs

---
 report2/paper.md | 107 +++++++++++++++++++++++++++++++++++++++++++++----------
 1 file changed, 88 insertions(+), 19 deletions(-)

(limited to 'report2/paper.md')

diff --git a/report2/paper.md b/report2/paper.md
index e4c3ec0..d9bfd10 100755
--- a/report2/paper.md
+++ b/report2/paper.md
@@ -1,13 +1,17 @@
-# Formulation of the Addresssed Machine Learning Problem
-
-## Probelm Definition
+# 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.
 
-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.
+# Formulation of the Addresssed Machine Learning Problem
 
-## Dataset - CUHK03
+## CUHK03
 
 The dataset CUHK03 contains 14096 pictures of people captured from two 
 different cameras. The feature vectors used come from passing the
@@ -19,6 +23,13 @@ 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
@@ -35,7 +46,7 @@ EXPLAIN KNN BRIEFLY
 \begin{figure}
 \begin{center}
 \includegraphics[width=20em]{fig/baseline.pdf}
-\caption{Top K Accuracy for Nearest Neighbour classification}
+\caption{Recognition accuracy of baseline Nearest Neighbor @rank k}
 \label{fig:baselineacc}
 \end{center}
 \end{figure}
@@ -43,31 +54,26 @@ EXPLAIN KNN BRIEFLY
 \begin{figure}
 \begin{center}
 \includegraphics[width=22em]{fig/eucranklist.png}
-\caption{Top 10 ranklist for 5 probes}
+\caption{Ranklist @rank10 generated for 5 query images}
 \label{fig:eucrank}
 \end{center}
 \end{figure}
 
+
 # Suggested Improvement
 
 \begin{figure}
 \begin{center}
 \includegraphics[width=24em]{fig/ranklist.png}
-\caption{Top 10 ranklist (improved method) 5 probes}
+\caption{Ranklist (improved method) @rank10 generated for 5 query images}
 \label{fig:ranklist2}
 \end{center}
 \end{figure}
 
-
-TODO:
-~~
-s/kNN/NN/
-~~
-
 \begin{figure}
 \begin{center}
 \includegraphics[width=20em]{fig/comparison.pdf}
-\caption{Top K Accurarcy}
+\caption{Comparison of recognition accuracy @rank k (KL=0.3,K1=9,K2=3)}
 \label{fig:baselineacc}
 \end{center}
 \end{figure}
@@ -75,11 +81,74 @@ s/kNN/NN/
 \begin{figure}
 \begin{center}
 \includegraphics[width=17em]{fig/pqvals.pdf}
-\caption{Top 1 Accuracy when k1 and k2}
+\caption{Identification accuracy varying K1 and K2}
 \label{fig:pqvals}
 \end{center}
 \end{figure}
 
+\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/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}
 
 # Conclusion
 
-- 
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