STUDY COMPARISON OF SVM-, K-NN- AND BACKPROPAGATION-BASED CLASSIFIER FOR IMAGE RETRIEVAL
Abstract: Classification is a
method for compiling data systematically according to the rules that have been
set previously. In recent years classification method has been proven to help
many people’s work, such as image classification, medical biology, traffic
light, text classification etc. There are many methods to solve classification
problem. This variation method makes the researchers find it difficult to
determine which method is best for a problem. This framework is aimed to
compare the ability of classification methods, such as Support Vector Machine
(SVM), K-Nearest Neighbor (K-NN), and Backpropagation, especially in study
cases of image retrieval with five category of image dataset. The result shows
that K-NN has the best average result in accuracy with 82%. It is also the
fastest in average computation time with 17,99 second during retrieve session
for all categories class. The Backpropagation, however, is the slowest among
three of them. In average it needed 883 second for training session and 41,7
second for retrieve session.
Penulis: Muhammad Athoillah,
M. Isa Irawan, Elly Matul Imah
Journal Code: jptkomputergg150003
