Learning a Frequency–Based Weighting for Medical Image Classification

Abstract

This article describes the use of a frequency–based weighting developed for image retrieval to perform automatic annotation of images (medical and non–medical). The techniques applied are based on a simple tf/idf (term frequency, inverse document frequency) weighting scheme of GIFT (GNU Image Finding Tool), which is augmented by feature weights extracted from training data. The additional weights represent a measure of discrimination by taking into account the number of occurrences of the features in pairs of images of the same class or in pairs of images from different classes. The approach is fit to the image classification task by pruning parts of the training data. Further investigations were performed showing that weightings lead to significantly worse classification quality in certain feature domains. A classifier using a mixture of tf/idf weighted scoring, learned feature weights, and regular Euclidean distance gave best results using only the simple features. Using the aspect–ratio of images as feature improved results significantly.