A-Lamp: Adaptive Layout-Aware Multi-Patch Deep Convolutional Neural
Network for Photo Aesthetic Assessment
Abstract
Deep convolutional neural networks (CNN) have recently been shown to generate promising results for aesthetics assessment. However, the performance of these deep
CNN methods is often compromised by the constraint that
the neural network only takes the fixed-size input. To accommodate this requirement, input images need to be transformed via cropping, warping, or padding, which often alter
image composition, reduce image resolution, or cause image distortion. Thus the aesthetics of the original images
is impaired because of potential loss of fine grained details
and holistic image layout. However, such fine grained details and holistic image layout is critical for evaluating an
image’s aesthetics. In this paper, we present an Adaptive
Layout-Aware Multi-Patch Convolutional Neural Network
(A-Lamp CNN) architecture for photo aesthetic assessment.
This novel scheme is able to accept arbitrary sized images,
and learn from both fined grained details and holistic image layout simultaneously. To enable training on these hybrid inputs, we extend the method by developing a dedicated
double-subnet neural network structure, i.e. a Multi-Patch
subnet and a Layout-Aware subnet. We further construct an
aggregation layer to effectively combine the hybrid features
from these two subnets. Extensive experiments on the largescale aesthetics assessment benchmark (AVA) demonstrate
significant performance improvement over the state-of-theart in photo aesthetic assessment