From 333d158dd0bac1e1fee86c6399f763dea22a90ea Mon Sep 17 00:00:00 2001 From: Vasil Zlatanov Date: Tue, 12 Feb 2019 17:14:56 +0000 Subject: Write bag-of-words section --- report/paper.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) (limited to 'report/paper.md') diff --git a/report/paper.md b/report/paper.md index 7453289..ac72f2b 100644 --- a/report/paper.md +++ b/report/paper.md @@ -11,11 +11,13 @@ Caltech dataset. ## Vocabulary size -The number of clusters or the number of centroids determine the vocabulary size. +The number of clusters or the number of centroids determine the vocabulary size when creating the codebook with the K-means the method. Each descriptor is mapped to the nearest centroid, and each descriptor belonging to that cluster is mapped to the same *visual word*. This allows similar descriptors to be mapped to the same word, allowing for comparison through bag-of-words techniques. -## Bag-of-words histograms of example training/testing images +## Bag-of-words histogram quantisation of descriptor vectors -Looking at picture \ref{fig:histo_te} +An example histogram for training image shown on figure {fig:histo_tr}, computed with a vocubulary size of 100. A corresponding testing image of the same class is shown in figure \ref{fig:histo_te}. The histograms appear to have similar counts for the same words, demonstrating they had a descriptors which matched the *keywowrds* in similar proportions. We later look at the effect of the vocubalary size (as determined by the number of K-mean centroids) on the classificaiton accuracy in figure \ref{fig:km_vocsize}. + +The time complexity of quantisation with a K-means codebooks is $O(n^{dk+1))$ , where n is the number of entities to be clustered, d is the dimension and k is the cluster count @cite[km-complexity]. As the computation time is high, the tests we use a subsample of descriptors to compute the centroids. An alternative method is NUNZIO PUCCI WRITE HERE \begin{figure}[H] \begin{center} @@ -35,8 +37,6 @@ Looking at picture \ref{fig:histo_te} \end{center} \end{figure} -## Vector quantisation process - # RF classifier ## Hyperparameters tuning -- cgit v1.2.3-54-g00ecf