Abstract:
Rice is the most widely consumed staple food in
Sri Lanka. In this paper, we present a comparative study of
different features (SIFT, Multi-resolution Local Patterns, Local
Color Histograms, and Random Projections) and feature encoding
approaches (Bag-of-visual-words, Sparse Coding, Vector of
Locally Aggregated Gradients, and Fisher Vectors) for classifying
images containing rice grains. By analysing the performance of
a classification model with two-fold cross validation on a dataset
of 1000 images containing ten rice categories, we show that
SIFT features with Fisher Vector encoding or with Vector of
Locally Aggregated Gradients produces the best result (mean
class accuracy of 97:9 0:5). We found that increasing the size of
the dictionary generally improves the classification performance
for all the feature encoding approaches. The dataset we use is
made public, and it can be accessed via http://www.csc.jfn.ac.lk/
index.php/dataset/.
