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authornunzip <np.scarh@gmail.com>2018-12-12 21:23:14 +0000
committernunzip <np.scarh@gmail.com>2018-12-12 21:23:14 +0000
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-rwxr-xr-xreport2/metadata.yaml3
-rwxr-xr-xreport2/paper.md7
2 files changed, 5 insertions, 5 deletions
diff --git a/report2/metadata.yaml b/report2/metadata.yaml
index 20375f9..5500c63 100755
--- a/report2/metadata.yaml
+++ b/report2/metadata.yaml
@@ -6,6 +6,9 @@ author:
numbersections: yes
lang: en
babel-lang: english
+nocite: |
+ @deepreid, @sklearn
+
abstract: |
This report analyses distance metrics learning techniques with regards to
identification accuracy for the dataset CUHK03. The baseline method used for
diff --git a/report2/paper.md b/report2/paper.md
index 6358445..20bd053 100755
--- a/report2/paper.md
+++ b/report2/paper.md
@@ -130,7 +130,7 @@ repository complimenting this paper.
The approach addressed to improve the identification performance is based on
k-reciprocal reranking. The following section summarizes the idea behind
-the method illustrated in **REFERENCE PAPER**.
+the method illustrated in reference @rerank-paper.
We define $N(p,k)$ as the top k elements of the ranklist generated through NN,
where p is a query image. The k reciprocal ranklist, $R(p,k)$ is defined as the
@@ -185,7 +185,7 @@ in steps of 0.1. The results obtained through this approach suggest: $k_{1_{opt}
Reranking achieves better results than the other baseline methods analyzed both as $top k$
accuracy and mean average precision.
It is also necessary to estimate how precise the ranklist generated is.
-For this reason an additional method of evaluation is introduced: mAP.
+For this reason an additional method of evaluation is introduced: mAP. See reference @mAP.
It is possible to see in figure \ref{fig:ranklist2} how the ranklist generated for the same five queries of figure \ref{fig:eucrank}
has improved for the fifth query. The mAP improves from 47% to 61.7%.
@@ -237,6 +237,3 @@ training are close to the ones for the local maximum of gallery and query.
# References
-# Appendix
-
-