Merge branch 'master' of skozl.com:e4-vision

1 files changed, 38 insertions, 2 deletions

diff --git a/report/paper.md b/report/paper.md
index 5f23d05..45d7eb2 100644
--- a/report/paper.md
+++ b/report/paper.md
@@ -1,6 +1,42 @@
-# Chapter I 
+# K-means codebook 
 
-## Sub-chapter I
+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
+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: 
+
+## Vocabulary size 
+
+## Bag-of-words histograms of example training/testing images
+
+## Vector quantisation process
+
+# RF classifier 
+
+Train and test Random Forest using the training and testing data set in the form of bag-of-words
+obtained in Q1. Change the RF parameters (including the number of trees, the depth of trees, the
+degree of randomness parameter, the type of weak-learners: e.g. axis-aligned or two-pixel test),
+and show and discuss the results:
+
+## recognition accuracy, confusion matrix,
+
+## example success/failures,
+
+## time-efficiency of training/testing, 
+
+## impact of the vocabulary size on classification accuracy. 
+
+# RF codebook
+
+In Q1, replace the K-means with the random forest codebook, i.e. applying RF to 128 dimensional
+descriptor vectors with their image category labels, and using the RF leaves as the visual
+vocabulary. With the bag-of-words representations of images obtained by the RF codebook, train
+and test Random Forest classifier similar to Q2. Try different parameters of the RF codebook and
+RF classifier, and show/discuss the results in comparison with the results of Q2, including the
+vector quantisation complexity. 
 
 # References