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| author | nunzip <np.scarh@gmail.com> | 2019-02-15 16:41:07 +0000 |
|---|---|---|
| committer | nunzip <np.scarh@gmail.com> | 2019-02-15 16:41:07 +0000 |
| commit | 4f9214360fffadd86fb767f3b2322d657567851d (patch) | |
| tree | 3ed32dd02fe95db7b240b79ec5103dab7dd824a9 | |
| parent | 9c3582f77793b6c354ce64e584905e239ad3daf8 (diff) | |
| download | e4-vision-4f9214360fffadd86fb767f3b2322d657567851d.tar.gz e4-vision-4f9214360fffadd86fb767f3b2322d657567851d.tar.bz2 e4-vision-4f9214360fffadd86fb767f3b2322d657567851d.zip | |
Remove line from failed merge
| -rw-r--r-- | report/paper.md | 4 |
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diff --git a/report/paper.md b/report/paper.md index 43411bc..e44444b 100644 --- a/report/paper.md +++ b/report/paper.md @@ -10,11 +10,7 @@ Both training and testing use 15 randomly selected images from the 10 available ## Vocabulary size -<<<<<<< HEAD The number of clusters or the number of centroids determines the vocabulary size when creating the codebook with the K-means method. Each descriptor is mapped to the nearest centroid, and each descriptor belonging to that cluster is mapped to the same *visual word*. This allows similar descriptors to be mapped to the same word, allowing for comparison through bag-of-words techniques. -======= -The number of clusters or the number of centroids determine the vocabulary size when creating a codebook with the K-means the method. Each descriptor is mapped to the nearest centroid, and each descriptor belonging to that cluster is mapped to the same *visual word*. This allows similar descriptors to be mapped to the same word, allowing for comparison through bag-of-words techniques. ->>>>>>> b052905b323972a69fbf04fd621a6d88c44c40a1 ## Bag-of-words histogram of descriptor vectors |