Journal
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Volume 41, Issue 7, Pages 1531-1544Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TPAMI.2018.2840724
Keywords
Photo cropping; attention box prediction; aesthetics assessment; deep learning
Funding
- Beijing Natural Science Foundation [4182056]
- National Basic Research Program of China [2013CB328805]
- Fok Ying-Tong Education Foundation for Young Teachers
- US NSF [1618398, 1449860, 1350521]
- Direct For Computer & Info Scie & Enginr
- Division Of Computer and Network Systems [1449860] Funding Source: National Science Foundation
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We study the problem of photo cropping, which aims to find a cropping window of an input image to preserve as much as possible its important parts while being aesthetically pleasant. Seeking a deep learning-based solution, we design a neural network that has two branches for attention box prediction (ABP) and aesthetics assessment (AA), respectively. Given the input image, the ABP network predicts an attention bounding box as an initial minimum cropping window, around which a set of cropping candidates are generated with little loss of important information. Then, the AA network is employed to select the final cropping window with the best aesthetic quality among the candidates. The two sub-networks are designed to share the same full-image convolutional feature map, and thus are computationally efficient. By leveraging attention prediction and aesthetics assessment, the cropping model produces high-quality cropping results, even with the limited availability of training data for photo cropping. The experimental results on benchmark datasets clearly validate the effectiveness of the proposed approach. In addition, our approach runs at 5 fps, outperforming most previous solutions. The code and results are available at: https://github.com/shenjianbing/DeepCropping.
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