Hierarchical classification using a frequency-based weighting and simple visual features

This article describes the use of a frequency-based weighting scheme using low level visual features developed for image retrieval to perform a hierarchical classification of medical images. The techniques are based on a classical tf/idf (term frequency, inverse document frequency) weighting scheme of the GIFT (GNU Image Finding Tool), and perform classification based on kNN (k-Nearest Neighbors) and voting-based approaches. The features used by the GIFT are very simple giving a global description of the images and local information on fixed regions both for colors and textures. We reused a similar technique as in previous years of ImageCLEF to have a baseline for the retrieval performance over the three years of the medical image annotation task. This allows showing the clear increase in quality of participating research systems over the years.Subsequently, we optimized the retrieval results based on the simple technology used by varying the feature space, the classification method (varying number of neighbors, various voting schemes) and by adding new information such as aspect ratio, which has shown to work well in the past. The results show that the techniques we use have several problems that could not be fully solved through the applied optimizations. Still, optimizations improved results enormously from an error value of 228 to below 150. As a baseline to show the progress of techniques over the years it also works well. Aspect ratio shows to be an important factor to improve results. Performing classification on an axis level performs better than using the entire hierarchy code or not taking hierarchy into account at all. To further improve results, the use of more suitable visual features such as patch histograms or salient point features seems necessary. Small distortions of images of the same class have to be taken into account for very good results. Still, without using any learning technique and high level visual features, the approach performs reasonably well.