To provide good results, image search engines need to rank not just the most relevant images, but also the highest quality images. To surface beautiful pictures, existing computational aesthetic models are trained with datasets from photo contest websites, dominated by professional photos. Such models fail completely in real web scenarios, where images are extremely diverse in terms of quality and type (e.g. drawings, clip-art, etc). This work aims at bridging and understanding this "aesthetic gap". We collect a dataset of around 100K web images with `quality' and `type' (photo vs non-photo) annotations. We design a set of visual features to describe image pictorial characteristics, and deeply analyse the peculiar beauty of web images as opposed to appealing professional images. Finally, we build a set of computational aesthetic frameworks based on deep learning and hand-crafted features that take into account the diverse quality of web images, and show that they significantly outperform traditional computational aesthetics methods on our dataset.

User participation in online communities is driven by the intertwinement of the social network structure with the crowd-generated content that flows along its links. These aspects are rarely explored jointly and at scale. By looking at how users generate and access pictures of varying beauty on Flickr, we investigate how the production of quality impacts the dynamics of online social systems. We develop a deep learning computer vision model to score images according to their aesthetic value and we validate its output through crowdsourcing. By applying it to over 15 B Flickr photos, we study for the first time how image beauty is distributed over a large-scale social system. Beautiful images are evenly distributed in the network, although only a small core of people get social recognition for them. To study the impact of exposure to quality on user engagement, we set up matching experiments aimed at detecting causality from observational data. Exposure to beauty is double-edged: following people who produce high-quality content increases one's probability of uploading better photos; however, an excessive imbalance between the quality generated by a user and the user's neighbors leads to a decline in engagement. Our analysis has practical implications for improving link recommender systems.

In this paper, we describe an efficient method for creating mosaics from collections of videos. Our method is based on a utility maximization formulation which we optimize greedily. We employ a function for quickly estimating mosaics without computing image features that allows us to efficiently take greedy steps while still achieving user definable goals for mosaic quality. Indeed, we demonstrate using a number of single- and multi-video experiments that our approach can construct high-quality mosaics in only a fraction of the time required to perform the operations undertaken by existing video mosaicing algorithms. While we focus in this work on the application of panorama construction, our method has a wide range of applications, such as super-resolution, summary, and indexing.

In this paper, we describe the design of a 3 Giga-samples per second real-time multi-channel data-acquisition unit. We first introduce the technical challenges including synchronization, data transmission and printed circuit board design. Then we analyze the cause of issues in multi-channel synchronization, and develop a novel method to synchronize the channels and correctly save the data into RAM. We later discuss issues surrounding the unit's signal integrity and routing. We provide techniques and design procedures to properly mitigate these issues and guarantee the correct performance of the system. The DAU has been fabricated, and its performance is evaluated in this paper by sampling ultra-wide-band signals.