The Use of Self Organizing Map Method and Feature Selection in Image Database Classification System

TL;DR

This paper explores using Self Organizing Map (SOM) with PCA and LSA feature selection methods to classify images, finding PCA with 100 features yields the highest accuracy of 88%.

Contribution

It demonstrates that combining PCA with SOM improves image classification accuracy over LSA-based feature selection.

Findings

PCA with 100 features achieves 88% accuracy.

SOM effectively classifies images using selected features.

PCA outperforms LSA in this classification task.

Abstract

This paper presents a technique in classifying the images into a number of classes or clusters desired by means of Self Organizing Map (SOM) Artificial Neural Network method. A number of 250 color images to be classified as previously done some processing, such as RGB to grayscale color conversion, color histogram, feature vector selection, and then classifying by the SOM Feature vector selection in this paper will use two methods, namely by PCA (Principal Component Analysis) and LSA (Latent Semantic Analysis) in which each of these methods would have taken the characteristic vector of 50, 100, and 150 from 256 initial feature vector into the process of color histogram. Then the selection will be processed into the SOM network to be classified into five classes using a learning rate of 0.5 and calculated accuracy. Classification of some of the test results showed that the highest percentage of accuracy obtained when using PCA and the selection of 100 feature vector that is equal to 88%, compared to when using LSA selection that only 74%. Thus it can be concluded that the method fits the PCA feature selection methods are applied in conjunction with SOM and has an accuracy rate better than the LSA feature selection methods. Keywords: Color Histogram, Feature Selection, LSA, PCA, SOM.