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 .
Computational aesthetics is the branch of computer vision that studies how to automatically score images in terms of their photographic beauty. ( datta ) and ( ke2006design ) designed the first compositional features to distinguish amateur from professional photos. Computational aesthetics researchers have been developing dedicated discriminative visual features and attributes @cite_42 @cite_27 , generic semantic features @cite_7 @cite_36 , topic-specific models @cite_22 @cite_8 and effective learning frameworks @cite_16 to improve the quality of the aesthetics predictors. Aesthetic features have been also used to infer higher-level properties of images and videos, such as image affective value @cite_23 , image memorability @cite_30 , video creativity @cite_9 , and video interestingness @cite_57 @cite_59 . To our knowledge, this is the first time that image aesthetic predictors are used to expose high quality content from low-popular images in the context of social media.
@cite_30 @cite_22 @cite_7 @cite_8 @cite_36 @cite_9 @cite_42 @cite_57 @cite_27 @cite_23 @cite_59 @cite_16
2806817979 2080948161 2009678853 2529088810
The ability to rank the images based on their appearance finds many real-world applications, such as image retrieval or image album creation. Despite the recent dominance of deep learning methods in computer vision which often result in superior performance, they are not always the methods of choice because they lack interpretability. In this paper, we investigate the possibility of improving the image aesthetic inference of the convolutional neural networks with hand-designed features that rely on domain expertise in various fields. We perform a comparison of hand-crafted feature sets in their ability to predict fine-grained aesthetics scores on two image aesthetics data sets. We observe that even feature sets published earlier are able to compete with more recently published algorithms and, by combining the algorithms, a significant improvement in predicting image aesthetics is possible. By using a tree-based learner, we perform the feature elimination to understand the best performing features overall and across different image categories. Only roughly 15 and 8 of the features are needed to achieve full performance in predicting a fine-grained aesthetic score and binary classification, respectively. By combining the hand-crafted features with metafeatures that predict the quality of an image based on convolutional neural network features, the model performs better than a baseline VGG16 model. One can, however, achieve more significant improvement in both aesthetics score prediction and binary classification by fusing the hand-crafted features and the penultimate layer activations. Our experiments indicate an improvement up to 2.2 achieving current state-of-the-art binary classification accuracy on the aesthetic visual analysis data set when the hand-designed features are fused with activations from VGG16 and ResNet50 networks. Aesthetic image analysis is the study and assessment of the aesthetic properties of images. Current computational approaches to aesthetic image analysis either provide accurate or interpretable results. To obtain both accuracy and interpretability by humans, we advocate the use of learned and nameable visual attributes as mid-level features. For this purpose, we propose to discover and learn the visual appearance of attributes automatically, using a recently introduced database, called AVA, which contains more than 250,000 images together with their aesthetic scores and textual comments given by photography enthusiasts. We provide a detailed analysis of these annotations as well as the context in which they were given. We then describe how these three key components of AVA--images, scores, and comments--can be effectively leveraged to learn visual attributes. Lastly, we show that these learned attributes can be successfully used in three applications: aesthetic quality prediction, image tagging and retrieval. Effective visual features are essential for computational aesthetic quality rating systems. Existing methods used machine learning and statistical modeling techniques on handcrafted features or generic image descriptors. A recently-published large-scale dataset, the AVA dataset, has further empowered machine learning based approaches. We present the RAPID (RAting PIctorial aesthetics using Deep learning) system, which adopts a novel deep neural network approach to enable automatic feature learning. The central idea is to incorporate heterogeneous inputs generated from the image, which include a global view and a local view, and to unify the feature learning and classifier training using a double-column deep convolutional neural network. In addition, we utilize the style attributes of images to help improve the aesthetic quality categorization accuracy. Experimental results show that our approach significantly outperforms the state of the art on the AVA dataset. This article reviews recent computer vision techniques used in the assessment of image aesthetic quality. Image aesthetic assessment aims at computationally distinguishing high-quality from low-quality photos based on photographic rules, typically in the form of binary classification or quality scoring. A variety of approaches has been proposed in the literature to try to solve this challenging problem. In this article, we summarize these approaches based on visual feature types (hand-crafted features and deep features) and evaluation criteria (data set characteristics and evaluation metrics). The main contributions and novelties of the reviewed approaches are highlighted and discussed. In addition, following the emergence of deep-learning techniques, we systematically evaluate recent deep-learning settings that are useful for developing a robust deep model for aesthetic scoring.