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| author | Vasil Zlatanov <v@skozl.com> | 2019-02-12 20:45:18 +0000 |
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| committer | Vasil Zlatanov <v@skozl.com> | 2019-02-12 20:45:18 +0000 |
| commit | 28e951c4e7c590cfeded709539a59cfae8519e81 (patch) | |
| tree | 2884d9e08a0e89041b6a3e127a603bcfe45677c6 | |
| parent | b9ba9a17236c666dcb2119612e0783be95d2b465 (diff) | |
| download | e4-vision-28e951c4e7c590cfeded709539a59cfae8519e81.tar.gz e4-vision-28e951c4e7c590cfeded709539a59cfae8519e81.tar.bz2 e4-vision-28e951c4e7c590cfeded709539a59cfae8519e81.zip | |
Slight changes to conclusion
| -rw-r--r-- | report/paper.md | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/report/paper.md b/report/paper.md index 112bd6e..4c146c8 100644 --- a/report/paper.md +++ b/report/paper.md @@ -159,7 +159,7 @@ which is $O(\sqrt{D} N \log K)$ compared to $O(DNK)$ for K-means. Codebook mappi # Comparison of methods and conclusions -Overall K-means achieves slightly better accuracy that the RF-codebook at the expense of a higher execution time for training **(and testing???)**. +Overall we observe slightly higher accuracy when using K-means codebooks compared to RF codebook at the expense of a higher execution time for training and testing. As discussed in section I, due to the initialization process for optimal centroids placements, K-means can result unpreferable for large descriptors' sizes (in absence of methods for dimensionality reduction), @@ -168,7 +168,7 @@ and in many cases the increase in training time would not justify the minimum in For Caltech_101 RF-codebook seems to be the most suitable method to perform RF-classification. It is observable that for the particular dataset we are analysing the class *water_lilly* -is the one that gets misclassified the most, both in k-means and RF-codebook (refer to figures \ref{fig:km_cm} and \ref{fig:p3_cm}. This means that the features obtained +is the one that gets misclassified the most, both in K-means and RF codebooks (refer to figures \ref{fig:km_cm} and \ref{fig:p3_cm}. This means that the features obtained from this class do not guarantee very discriminative splits, hence the first splits in the trees will prioritize features taken from other classes. |