diff options
| -rw-r--r-- | report/metadata.yaml | 1 | ||||
| -rw-r--r-- | report/paper.md | 4 | ||||
| -rw-r--r-- | report/template.latex | 2 |
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} |