The dynamics of attention in social media tend to obey power laws. Attention concentrates on a relatively small number of popular items and neglecting the vast majority of content produced by the crowd. Although popularity can be an indication of the perceived value of an item within its community, previous research has hinted to the fact that popularity is distinct from intrinsic quality. As a result, content with low visibility but high quality lurks in the tail of the popularity distribution. This phenomenon can be particularly evident in the case of photo-sharing communities, where valuable photographers who are not highly engaged in online social interactions contribute with high-quality pictures that remain unseen. We propose to use a computer vision method to surface beautiful pictures from the immense pool of near-zero-popularity items, and we test it on a large dataset of creative-commons photos on Flickr. By gathering a large crowdsourced ground truth of aesthetics scores for Flickr images, we show that our method retrieves photos whose median perceived beauty score is equal to the most popular ones, and whose average is lower by only 1.5 .
Existing aesthetic ground truths are often derived from photo contest websites, such as DPChallenge.com @cite_49 or Photo.net @cite_6 , where (semi) professional photographers can rate the quality of their peers' images. The average quality and style of the images in such datasets is way higher than the typical picture quality in photo sharing sites, making them not suitable to train aesthetic models. Hybrid datasets @cite_54 that add lower-quality images to overcome this issue are also not good for training @cite_36 . In addition, social signals such as Flickr interestingness Flickr interestingness algorithm is secret, but it considers some metrics of social feedback. For more details refer to https: www.flickr.com explore interesting @cite_59 are often used as a proxy for aesthetics in that type of datasets. However, no quantitative evidence is given that neither the Flickr interestingness nor the popularity of the photographers are good proxies for image quality, which is exactly the research question we address. Crowdsourcing constitutes a reliable way to collect ground truths on image features @cite_37 , the only attempt to do it in the context of aesthetics has been limited in scope (faces) and very small-scale @cite_56 .
@cite_37 @cite_36 @cite_54 @cite_6 @cite_56 @cite_59 @cite_49
2345254477 2056380823 2080754665 2148848374
Recognizing what makes an image aesthetically pleasing is crucial to the effectiveness of many multimedia systems. Several works have attempted to build image aesthetic appeal predictors, and created their own set of ground truth data for the purpose, either by using rated images from photo sharing websites, or by asking a pool of users to rate images in lab or crowdsourcing experiments. Literature has shown that the way these experiments are conducted can influence their results: poor experimental setup can result in poorly reliable outcomes (i.e., highly imprecise aesthetic appeal measures). A question then arises whether the different choices made to collect ground truth of aesthetic appeal data are appropriate. In this paper, we propose a systematic study that looks into how different experimental environments and rating scales used to collect image aesthetic appeal ground truth data influence the reliability and repeatability of aesthetic appeal assessments. Our findings show that discrete and continuous scales with five-point absolute category rating labels yield more reliable results, with the continuous scale being more reliable for abstract images. We also show that image aesthetic appeal assessments could be repeatable across different experimental environments (i.e., lab and crowdsourcing). We finally formulate concrete recommendations to guide the collection of large sets of ground truth data for training models of aesthetic appeal appreciation. Automatically assessing photo quality from the perspective of visual aesthetics is of great interest in high-level vision research and has drawn much attention in recent years. In this paper, we propose content-based photo quality assessment using both regional and global features. Under this framework, subject areas, which draw the most attentions of human eyes, are first extracted. Then regional features extracted from both subject areas and background regions are combined with global features to assess photo quality. Since professional photographers adopt different photographic techniques and have different aesthetic criteria in mind when taking different types of photos (e.g., landscape versus portrait), we propose to segment subject areas and extract visual features in different ways according to the variety of photo content. We divide the photos into seven categories based on their visual content and develop a set of new subject area extraction methods and new visual features specially designed for different categories. The effectiveness of this framework is supported by extensive experimental comparisons of existing photo quality assessment approaches as well as our new features on different categories of photos. In addition, we propose an approach of online training an adaptive classifier to combine the proposed features according to the visual content of a test photo without knowing its category. Another contribution of this work is to construct a large and diversified benchmark dataset for the research of photo quality assessment. It includes 17,673 photos with manually labeled ground truth. This new benchmark dataset can be down loaded at http: mmlab.ie.cuhk.edu.hk CUHKPQ Dataset.htm. With the rise in popularity of digital cameras, the amount of visual data available on the web is growing exponentially. Some of these pictures are extremely beautiful and aesthetically pleasing, but the vast majority are uninteresting or of low quality. This paper demonstrates a simple, yet powerful method to automatically select high aesthetic quality images from large image collections. Our aesthetic quality estimation method explicitly predicts some of the possible image cues that a human might use to evaluate an image and then uses them in a discriminative approach. These cues or high level describable image attributes fall into three broad types: 1) compositional attributes related to image layout or configuration, 2) content attributes related to the objects or scene types depicted, and 3) sky-illumination attributes related to the natural lighting conditions. We demonstrate that an aesthetics classifier trained on these describable attributes can provide a significant improvement over baseline methods for predicting human quality judgments. We also demonstrate our method for predicting the “interestingness” of Flickr photos, and introduce a novel problem of estimating query specific “interestingness”. Most publicly available image quality databases have been created under highly controlled conditions by introducing graded simulated distortions onto high-quality photographs. However, images captured using typical real-world mobile camera devices are usually afflicted by complex mixtures of multiple distortions, which are not necessarily well-modeled by the synthetic distortions found in existing databases. The originators of existing legacy databases usually conducted human psychometric studies to obtain statistically meaningful sets of human opinion scores on images in a stringently controlled visual environment, resulting in small data collections relative to other kinds of image analysis databases. Toward overcoming these limitations, we designed and created a new database that we call the LIVE In the Wild Image Quality Challenge Database, which contains widely diverse authentic image distortions on a large number of images captured using a representative variety of modern mobile devices. We also designed and implemented a new online crowdsourcing system, which we have used to conduct a very large-scale, multi-month image quality assessment (IQA) subjective study. Our database consists of over 350 000 opinion scores on 1162 images evaluated by over 8100 unique human observers. Despite the lack of control over the experimental environments of the numerous study participants, we demonstrate excellent internal consistency of the subjective data set. We also evaluate several top-performing blind IQA algorithms on it and present insights on how the mixtures of distortions challenge both end users as well as automatic perceptual quality prediction models. The new database is available for public use at http: live.ece.utexas.edu research ChallengeDB index.html .