The photograph and our understanding of photography is ever changing and has transitioned from a world of unprocessed rolls of C-41 sitting in a fridge 50 years ago to sharing photos on the 1.5" screen of a point and shoot camera 10 years back. And today the photograph is again something different. The way we take photos is fundamentally different. We can view, share, and interact with photos on the device they were taken on. We can edit, tag, or "filter" photos directly on the camera at the same time the photo is being taken. Photos can be automatically pushed to various online sharing services, and the distinction between photos and videos has lessened. Beyond this, and more importantly, there are now lots of them. To Facebook alone more than 250 billion photos have been uploaded and on average it receives over 350 million new photos every day [6], while YouTube reports that 300 hours of video are uploaded every minute [22]. A back of the envelope estimation reports 10% of all photos in the world were taken in the last 12 months, and that was calculated already more than three years ago [8].Today, a large number of the digital media objects that are shared have been uploaded to services like Flickr or Instagram, which along with their metadata and their social ecosystem form a vibrant environment for finding solutions to many research questions at scale. Photos and videos provide a wealth of information about the universe, covering entertainment, travel, personal records, and various other aspects of life in general as it was when they were taken.
The MIR Flickr collection consists of 25000 high-quality photographic images of thousands of Flickr users, made available under the Creative Commons license. The database includes all the original user tags and EXIF metadata. Additionally, detailed and accurate annotations are provided for topics corresponding to the most prominent visual concepts in the user tag data. The rich metadata allow for a wide variety of image retrieval benchmarking scenarios.In this paper, we provide an overview of the various strategies that were devised for automatic visual concept detection using the MIR Flickr collection. In particular we discuss results from various experiments in combining social data and low-level content-based descriptors to improve the accuracy of visual concept classifiers. Additionally, we present retrieval results obtained by relevance feedback methods, demonstrating (i) how their performance can be enhanced using features based on visual concept classifiers, and (ii) how their performance, based on small samples, can be measured relative to their large sample classifier counterparts.Additionally, we identify a number of promising trends and ideas in visual concept detection. To keep the MIR Flickr collection upto-date on these developments, we have formulated two new initiatives to extend the original image collection. First, the collection will be extended to one million Creative Commons Flickr images. Second, a number of state-of-the-art content-based descriptors will be made available for the entire collection.
Social media responses to news have increasingly gained in importance as they can enhance a consumer's news reading experience, promote information sharing and aid journalists in assessing their readership's response to a story. Given that the number of responses to an online news article may be huge, a common challenge is that of selecting only the most interesting responses for display. This paper addresses this challenge by casting message selection as an optimization problem. We define an objective function which jointly models the messages' utility scores and their entropy. We propose a near-optimal solution to the underlying optimization problem, which leverages the submodularity property of the objective function. Our solution first learns the utility of individual messages in isolation and then produces a diverse selection of interesting messages by maximizing the defined objective function. The intuitions behind our work are that an interesting selection of messages contains diverse, informative, opinionated and popular messages referring to the news article, written mostly by users that have authority on the topic. Our intuitions are embodied by a rich set of content, social and user features capturing the aforementioned aspects. We evaluate our approach through both human and automatic experiments, and demonstrate it outperforms the state of the art. Additionally, we perform an in-depth analysis of the annotated "interesting" responses, shedding light on the subjectivity around the selection process and the perception of interestingness.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.