The CNN news footage datasets: Enabling supervision in image retrieval

Cagdas Bilen, Joaquin Zepeda, Patrick Pérez

Abstract: Image retrieval in large image databases is an important problem that drives a number of applications. Yet the use of supervised approaches that address this problem has been limited due to the lack of large labeled datasets for training. Hence, in this paper we introduce two new datasets composed of images extracted from publicly available videos from the Cable News Network (CNN). The proposed datasets are particularly suited to supervised learning for image retrieval and are larger than any other existing dataset of a similar nature. The datasets are further provided with a set of pre-computed, state-of-the-art image feature vectors, as well as baseline results. In order to facilitate research in this important topic, we also detail a generic, supervised learning formulation for image retrieval and a related stochastic solver.