Combination of Distances and Image Features for Clustering Image Data Bases

Daily, millions of pictures are released online but it is hard to analyze them automatically for marketing purposes. This paper tries to show how methods from the content-based image retrieval could be used to classify image data and make them usable for marketing applications. There are a number of different image features which can be extracted from the images to calculate dissimilarities between them afterwards with different kinds of distance measures (Manjunath et al. 2001). We focus especially on mass-transportation-problems, like the Earth Mover’s Distance (EMD) (Rubner et al., Int J Comput Vis 40(2):99–121, 2000), because they fit the human perception on dissimilarities. Furthermore there are already some studies that show that they are robust to disturbances like changes in resolution, contrast, or noise (Frost and Baier, Algorithms from and for nature and life. Studies in classification, data analysis, and knowledge organization, vol 45. Springer, Heidelberg, 2013). We compare some approximations of the EMD (e.g., Pele and Werman 2009) with an approximation algorithm developed by ourselves. The aim is to find a combination of features and distances which allows to cluster large image data bases in a way that fits the human perception.