From 97acdd6ea9e378c90cf9a199e746ebca59a4d5e6 Mon Sep 17 00:00:00 2001 From: Vasil Zlatanov Date: Mon, 11 Feb 2019 17:47:09 +0000 Subject: Add histogram fig --- report/fig/km-histogram.pdf | Bin 0 -> 13076 bytes report/paper.md | 23 ++++++++++++----------- 2 files changed, 12 insertions(+), 11 deletions(-) create mode 100644 report/fig/km-histogram.pdf (limited to 'report') diff --git a/report/fig/km-histogram.pdf b/report/fig/km-histogram.pdf new file mode 100644 index 0000000..f459978 Binary files /dev/null and b/report/fig/km-histogram.pdf differ 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 -- cgit v1.2.3-54-g00ecf