Photozilla: A Large-Scale Photography Dataset and Visual Embedding for 20 Photography Styles
This work addresses the need for automated style classification in digital photography, which is incremental as it builds on existing classification methods with a new dataset and adaptation technique.
The authors tackled the problem of classifying photography styles by introducing a large-scale dataset of over 990k images across 10 styles, achieving ~96% accuracy with classification models, and developed a novel Siamese-based network that adapts to unseen styles with only 25 training samples, achieving over 68% accuracy for 10 other styles.
The advent of social media platforms has been a catalyst for the development of digital photography that engendered a boom in vision applications. With this motivation, we introduce a large-scale dataset termed 'Photozilla', which includes over 990k images belonging to 10 different photographic styles. The dataset is then used to train 3 classification models to automatically classify the images into the relevant style which resulted in an accuracy of ~96%. With the rapid evolution of digital photography, we have seen new types of photography styles emerging at an exponential rate. On that account, we present a novel Siamese-based network that uses the trained classification models as the base architecture to adapt and classify unseen styles with only 25 training samples. We report an accuracy of over 68% for identifying 10 other distinct types of photography styles. This dataset can be found at https://trisha025.github.io/Photozilla/