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author | nunzip <np.scarh@gmail.com> | 2018-12-12 23:14:18 +0000 |
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committer | nunzip <np.scarh@gmail.com> | 2018-12-12 23:14:18 +0000 |
commit | 88ed5d2fec953107584fb53fefd9094dac6dec38 (patch) | |
tree | 91d92943d1fa611f888653496b54f782be6e6247 /report2/paper.md | |
parent | f71eb06aecc0d0450bdec1dd0be18d6d6195f640 (diff) | |
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diff --git a/report2/paper.md b/report2/paper.md index 20bd053..a961be0 100755 --- a/report2/paper.md +++ b/report2/paper.md @@ -180,6 +180,29 @@ This is done through a simple multi-direction search algorithm followed by exhau $k_{1_{opt}}$ 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: $k_{1_{opt}}=9, k_{2_{opt}}=3, 0.1\leq\lambda_{opt}\leq 0.3$. +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) KL=0.3} +\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) K1=9, K2=3} +\label{fig:lambda} +\end{center} +\end{figure} + + ## k-reciprocal Reranking Evaluation Reranking achieves better results than the other baseline methods analyzed both as $top k$ @@ -211,28 +234,6 @@ 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) KL=0.3} -\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) K1=9, K2=3} -\label{fig:lambda} -\end{center} -\end{figure} - # Conclusion # References |