A-Lamp: Adaptive Layout-Aware Multi-Patch Deep Convolutional Neural Network for Photo Aesthetic Assessment (1704.00248v1)

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 large-scale aesthetics assessment benchmark (AVA) demonstrate significant performance improvement over the state-of-the-art in photo aesthetic assessment.

• The paper presents a dual-subnet architecture that combines adaptive multi-patch selection with a layout-aware attribute graph to preserve both fine-grained details and holistic composition.
• It improves aesthetic categorization accuracy from 81.7% to 82.5% on the AVA dataset by mitigating fixed-size input limitations.
• The study highlights the model’s potential for broader computer vision applications such as style classification, object recognition, and scene categorization.

Overview of A-Lamp: Adaptive Layout-Aware Multi-Patch Deep Convolutional Neural Network for Photo Aesthetic Assessment

The research paper introduces a novel deep convolutional neural network architecture named A-Lamp, designed specifically for the task of photo aesthetic assessment. It addresses the constraints of traditional CNNs that necessitate fixed-size image inputs, which may lead to altered image composition and impaired aesthetic evaluation. A-Lamp mitigates these issues by processing images of arbitrary sizes, thereby preserving both fine-grained details and holistic image layouts critical for assessing aesthetics.

The proposed approach involves a dual-subnet architecture. The Multi-Patch subnet leverages an adaptive patch selection strategy to retain and process important image patches, focusing on details and pattern diversity without losing critical information through unwanted transformations. This adaptation seeks to enhance both training efficiency and prediction accuracy, as witnessed by an improvement to 81.7% accuracy in photo aesthetic quality categorization as compared to earlier models utilizing random cropping strategies.

Meanwhile, the Layout-Aware subnet constructs and utilizes an Attribute Graph model to grasp comprehensive layout representations by linking object-specific attributes with global scene attributes. This architectural addition is crucial for representing intricate photographical principles and image compositions, ensuring that images are assessed for their overall aesthetic quality rather than solely focusing on localized features.

Experimental validation on the large-scale AVA dataset demonstrates that A-Lamp significantly outperforms other state-of-the-art models. Specifically, the model achieves an impressive 82.5% accuracy and effectively responds to changes in image layout as well as fine details. This indicates a higher sensitivity of A-Lamp towards variations caused by image transformations, highlighting its robustness in aesthetic assessment.

Furthermore, the paper explores content-based image aesthetic analysis through evaluation across various categories such as portrait, still-life, and landscape, among others. A-Lamp leads in most categories, particularly where the maintenance of fine-grained details is pivotal, thus validating the effectiveness of its dual-subnet approach.

The implications of A-Lamp are broad and significant, as the architecture can be extended beyond aesthetic assessment to other computer vision domains such as style classification, object recognition, and scene classification. The adaptive processing model of multiple image sizes and varying compositions set forth by this research paves the way for future explorations in AI-driven image analysis tasks, emphasizing the blend of global and local feature learning for intricate image categorization challenges.