diff options
author | Vasil Zlatanov <v@skozl.com> | 2019-02-11 17:47:09 +0000 |
---|---|---|
committer | Vasil Zlatanov <v@skozl.com> | 2019-02-11 17:47:09 +0000 |
commit | 97acdd6ea9e378c90cf9a199e746ebca59a4d5e6 (patch) | |
tree | 3ac6578f5940c4b70ed9c0627a8c0da9b0c7f3d2 /report | |
parent | 586909e40569cd32f398d4360ba67876da10887f (diff) | |
download | e4-vision-97acdd6ea9e378c90cf9a199e746ebca59a4d5e6.tar.gz e4-vision-97acdd6ea9e378c90cf9a199e746ebca59a4d5e6.tar.bz2 e4-vision-97acdd6ea9e378c90cf9a199e746ebca59a4d5e6.zip |
Add histogram fig
Diffstat (limited to 'report')
-rw-r--r-- | report/fig/km-histogram.pdf | bin | 0 -> 13076 bytes | |||
-rw-r--r-- | report/paper.md | 23 |
2 files changed, 12 insertions, 11 deletions
diff --git a/report/fig/km-histogram.pdf b/report/fig/km-histogram.pdf Binary files differnew file mode 100644 index 0000000..f459978 --- /dev/null +++ b/report/fig/km-histogram.pdf diff --git a/report/paper.md b/report/paper.md index 037d0df..d8e4fca 100644 --- a/report/paper.md +++ b/report/paper.md @@ -1,17 +1,18 @@ -# K-means codebook - -We randomly select 100k descriptors for K-means clustering for building the visual vocabulary -(due to memory issue). Open the main_guideline.m and select/load the dataset. -``` -[data_train, data_test] = getData('Caltech'); -``` -Set 'showImg = 0' in getData.m if you want to stop displaying training and testing images. -Complete getData.m by writing your own lines of code to obtain the visual vocabulary and the -bag-of-words histograms for both training and testing data. Show, measure and -discuss the followings: +# Codebooks + +## K-means codebook + +A common technique for codebook generation involves utilising K-means clustering on a sample of the +image descriptors. In this way descriptors may be mapped to *visual* words which lend themselves to +binning and therefore the creation of bag-of-words histograms for the use of classification. + +In this courseworok 100-thousand descriptors have been selected to build the visual vocabulary from the +Caltech dataset. ## Vocabulary size +The number of clusters or the number of centroids determine the vocabulary size. + ## Bag-of-words histograms of example training/testing images ## Vector quantisation process |