Despite recent progress, computational visual aesthetic is still challenging. Image cropping, which refers to the removal of unwanted scene areas, is an important step to improve the aesthetic quality of an image. However, it is challenging to evaluate whether cropping leads to aesthetically pleasing results because the assessment is typically subjective. In this paper, we propose a novel cascaded cropping regression (CCR) method to perform image cropping by learning the knowledge from professional photographers. The proposed CCR method improves the convergence speed of the cascaded method, which directly uses random-ferns regressors. In addition, a two-step learning strategy is proposed and used in the CCR method to address the problem of lacking labelled cropping data. Specifically, a deep convolutional neural network (CNN) classifier is first trained on large-scale visual aesthetic datasets. The deep CNN model is then designed to extract features from several image cropping datasets, upon which the cropping bounding boxes are predicted by the proposed CCR method. Experimental results on public image cropping datasets demonstrate that the proposed method significantly outperforms several state-of-the-art image cropping methods.
Methods of the second category rely on the aesthetic evaluation of cropping results. Machine learning methods are often used to identify the aesthetic of cropped images. Moreover, these methods also consider the spatial distribution of elements in an image to obtain an optimized arrangement of the image elements. Representative works are @cite_6 @cite_4 @cite_13 . However, aesthetic based methods are sensitive to evaluating the attractiveness of a cropped image, and therefore these methods focus on what remains in the cropped image. To overcome the above problem, Yan et al. @cite_25 @cite_0 propose an image cropping method belonging to the third category of image cropping methods, namely an experience-based method. In their method, they construct several cropping datasets, which are annotated by three professional photographers, for image cropping. Various handcrafted features are then extracted for regressing the cropping values annotated by the professional photographers. This method emphasizes professionals' experience or the change caused by the manipulations of image cropping. A drawback is that handcrafted features are often limited and may lack useful information for high-level computer vision tasks.
@cite_4 @cite_6 @cite_0 @cite_13 @cite_25
2023081159 2013339738 2061167180 2142785776
In this paper, we propose an intelligent photography system, which automatically and professionally generates recommends user-favorite photo(s) from a wide view or a continuous view sequence. This task is quite challenging given that the evaluation of photo quality is under-determined and usually subjective. Motivated by the recent prevalence of online media, we present a solution y mining the underlying knowledge and experience of the photographers from massively crawled professional photos (about 100,000 images, which are highly ranked by users) of those popular photo sharing websites, e.g. Flickr.com. Generally far contexts are critical in characterizing the composition rules for professional photos, and thus we present a method called omni-range context modeling to learn the patch object spatial correlation distribution for the concurrent patch object pair of arbitrary distance. The learned photo omni-range context priors then serve as rules to guide the composition of professional photos. When a wide view is fed into the system, these priors are utilized together with other cues (e.g., placements of faces at different poses, patch number, etc) to form a posterior probability formulation for professional sub-view finding. Moreover, this system can function as intelligent professionalview guider based on real-time view quality assessment and the embedded compass (for recording capture direction). Beyond the salient areas targeted by most existing view recommendation algorithms, the proposed system targets at professional photo composition. Qualitative experiments as well as comprehensive user studies well demonstrate the validity and efficiency of the proposed omnirange context learning method as well as the automatic view finding framework. This paper proposes a novel method for automatically cropping a photo using a quality classifier that assesses whether the cropped region is agreeable to users. We statistically build this quality classifier using large photo collections available on websites where people manually insert quality scores to photos. We first trim the original image and then decide on the candidates for cropping. We find the cropped region with the highest quality score by applying the quality classifier to the candidates. Current automatic photo cropping techniques search for attention grabbing regions that consist of salient pixels from the original photo. They are not always pleasant to users because they do not take into account the quality of the cropped region. Our method with the quality classifier outperforms a state-of-the-art method that takes into consideration only the user's attention for automatic photo cropping. Image cropping is a common operation used to improve the visual quality of photographs. In this paper, we present an automatic cropping technique that accounts for the two primary considerations of people when they crop: removal of distracting content, and enhancement of overall composition. Our approach utilizes a large training set consisting of photos before and after cropping by expert photographers to learn how to evaluate these two factors in a crop. In contrast to the many methods that exist for general assessment of image quality, ours specifically examines differences between the original and cropped photo in solving for the crop parameters. To this end, several novel image features are proposed to model the changes in image content and composition when a crop is applied. The effectiveness of each feature is empirically analyzed in determining a final feature set for crop computation. Our experiments demonstrate improvements of our method over recent cropping algorithms on a broad range of images. As one of the most basic photo manipulation processes, photo cropping is widely used in the printing, graphic design, and photography industries. In this paper, we introduce graphlets (i.e., small connected subgraphs) to represent a photo's aesthetic features, and propose a probabilistic model to transfer aesthetic features from the training photo onto the cropped photo. In particular, by segmenting each photo into a set of regions, we construct a region adjacency graph (RAG) to represent the global aesthetic feature of each photo. Graphlets are then extracted from the RAGs, and these graphlets capture the local aesthetic features of the photos. Finally, we cast photo cropping as a candidate-searching procedure on the basis of a probabilistic model, and infer the parameters of the cropped photos using Gibbs sampling. The proposed method is fully automatic. Subjective evaluations have shown that it is preferred over a number of existing approaches.