Indexing 100M Images with Deep Features and MI-File Giuseppe Amato, Fabrizio Falchi, Claudio Gennaro, and Fausto Rabitti ISTI-CNR via G. Moruzzi, 1 - 56124 Pisa, Italy <name>.<surname>@isti.cnr.it Abstract. In the context of the Multimedia Commons initiative, we extracted and indexed deep features of about 100M images uploaded on Flickr between 2004 and 2014 and published under a Creative Commons commercial or noncommercial license. The extracted features and an on- line demo built using the MI-File approximated data structure are both publicly available. The online CBIR system demonstrates the effective- ness of the deep features and the efficiency of the indexing approach. Keywords: Deep Features, MI-File, Content-Based Image Retrieval, Similarity Search 1 Introduction Deep Convolutional Neural Networks (DCNNs) have recently shown impressive performance on a number of multimedia information retrieval tasks [6, 9, 4]. In particular, the activation of the DCNN hidden layers has been also used in the context of transfer learning and conten-based image retrieval [3,8]. In fact, Deep Learning methods are “representation-learning methods with multiple levels of representation, obtained by composing simple but non-linear modules that each transform the representation at one level (starting with the raw input) into a representation at a higher, slightly more abstract level” [7]. These representations can be successfully used as features in generic recognition or visual similarity search tasks. In this paper we present a public online Content-Based Image Retrieval sys- tem indexing about 100M images. The dataset is the YFCC100M which is the largest Creative Commons image dataset available today. The deep features were extracted using a public available DCNN using the Caffe[5] framwork and can be downloaded from http://www.deepfeatures.org. The 4,096-dimensional fea- tures vectors were indexed using MI-File[1], a permutation-based approximated data structure. The online demo is available at http://mifile.deepfeatures.org. A screenshot of the web bases interface can be seen in Figure 1