%0 Journal Article
%T Unsupervised Detection of Outlier Images Using Multi-Order Image Transforms
%A Lior Shamir
%J Theory and Applications of Mathematics & Computer Science
%D 2013
%I Aurel Vlaicu University Editing House
%X The task of unsupervised detection of peculiar images has immediate applications to numerous scientific disciplines such as astronomy and biology. Here we describe a simple non-parametric method that uses multi-order image transforms for the purpose of automatic unsupervised detection of peculiar images in image datasets. The method is based on computing a large set of image features from the raw pixels and the first and second order of several combinations of image transforms. Then, the features are assigned weights based on their variance, and the peculiarity of each image is determined by its weighted Euclidean distance from the centroid such that the weights are computed from the variance. Experimental results show that features extracted from multi-order image transforms can be used to automatically detect peculiar images in an unsupervised fashion in different image datasets, including faces, paintings, microscopy images, and more, and can be used to find uncommon or peculiar images in large datasets in cases where the target image of interest is not known. The performance of the method is superior to general methods such as one-class SVM. Source code and data used in this paper are publicly available, and can be used as a benchmark to develop and compare the performance of algorithms for unsupervised detection of peculiar images.
%K Outlier detection
%K peculiar images
%K image analysis
%K image transform
%K multi-order transforms
%U http://www.uav.ro/applications/se/journal/index.php/TAMCS/article/view/70/51