How interesting images are: An atypicality approach for social networks

With the exponential growth in information, “Big Data,” a key question is what to do with this information. A classic possibility is to characterize it through statistics. The perspective in this paper is the opposite, namely that most of the value in the information is in the parts that deviates from the average, that are unusual, atypical. Think of art: The valuable paintings or writings are those that deviate from the norms, that are atypical. With the same perspective think of social networks: Flickr is an example that has a rating for images based on how interesting they are. In previous works, researchers introduced algorithms to find the interesting images based on human psychological taste; but in this paper, we are proposing a new method to rate an image based on their level of interestingness using atypicality, in which interestingness is equivalent to having atypical structure. The paper first discusses what exactly should be understood by “atypical.” It has to be a well defined theoretical concept corresponding to some intuitive idea of atypicality, which when applied gives useful results. This is followed by applying the notion of atypicality to the general discrete scenario with finite alphabet and then putting it in to the framework of the quantized wavelet image coding. Finally our proposed algorithm is applied on Labeled Faces in the Wild (LFW) database with 13,233 images in order to find the interesting ones.