研究目的
To develop a pairwise aesthetic comparison network (PAC-Net) for image aesthetic assessment that can handle the ambiguity and subjectivity of aesthetic criteria by learning relative aesthetic ranks between images and applying it to ranking and classification tasks.
研究成果
PAC-Net effectively addresses the challenges of image aesthetic assessment by leveraging pairwise comparisons and a novel loss function, achieving state-of-the-art performance in both ranking and classification tasks on standard datasets. Future work could explore extensions to more complex aesthetic criteria and larger-scale applications.
研究不足
The approach may be limited by the subjectivity inherent in aesthetic assessment, potential biases in the datasets, and the computational complexity of pairwise comparisons for large datasets. Optimization could focus on reducing inference time and handling very similar image pairs more effectively.
1：Experimental Design and Method Selection:
The methodology involves designing a Siamese network-based PAC-Net with aesthetic feature extraction using a CNN (GoogLeNet) and pairwise feature comparison using comparators. A novel aesthetic-adaptive cross entropy loss function is employed for training to focus on image pairs with larger score differences.
2：Sample Selection and Data Sources:
The experiments use the AVA and AADB datasets, which provide images with ground-truth aesthetic scores from human annotators. AVA has 235,599 training and 19,930 test images with scores in [1,10], and AADB has 8,500 training, 500 validation, and 1,000 test images with scores in [0,1].
3：1]. List of Experimental Equipment and Materials:
3. List of Experimental Equipment and Materials: A computer system with software for deep learning (e.g., frameworks supporting CNNs), the GoogLeNet model pre-trained on ImageNet, and the datasets.
4：Experimental Procedures and Operational Workflow:
Images are resized to 224x
5：The triplet network is trained with three branches sharing parameters, using the Adam optimizer with an initial learning rate of For testing, pairwise comparisons are made to rank images or classify them by comparing with reference images. Data Analysis Methods:
2 Performance is evaluated using Spearman's rank correlation coefficient for ranking and accuracy for classification, with statistical analysis based on the results.
独家科研数据包，助您复现前沿成果，加速创新突破
获取完整内容
