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-rw-r--r--report/metadata.yaml1
-rw-r--r--report/paper.md4
-rw-r--r--report/template.latex2
3 files changed, 5 insertions, 2 deletions
diff --git a/report/metadata.yaml b/report/metadata.yaml
index 906172a..d54434c 100644
--- a/report/metadata.yaml
+++ b/report/metadata.yaml
@@ -6,6 +6,7 @@ author:
numbersections: yes
lang: en
babel-lang: english
+fontsize: 10pt
abstract: |
This is a very good abstract.
diff --git a/report/paper.md b/report/paper.md
index b9abff5..037d0df 100644
--- a/report/paper.md
+++ b/report/paper.md
@@ -2,7 +2,9 @@
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'); % Select 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
diff --git a/report/template.latex b/report/template.latex
index 518752c..afc8358 100644
--- a/report/template.latex
+++ b/report/template.latex
@@ -1,4 +1,4 @@
-\documentclass[10pt,$if(fontsize)$$fontsize$,$endif$$if(lang)$$babel-lang$,$endif$$if(papersize)$$papersize$paper,$endif$$for(classoption)$$classoption$$sep$,$endfor$]{IEEEtran}
+\documentclass[$if(fontsize)$$fontsize$,$endif$$if(lang)$$babel-lang$,$endif$$if(papersize)$$papersize$paper,$endif$$for(classoption)$$classoption$$sep$,$endfor$]{IEEEtran}
$if(beamerarticle)$
\usepackage{beamerarticle} % needs to be loaded first
\usepackage[T1]{fontenc}