Photozilla: A Large-Scale Photography Dataset and Visual Embedding for 20 Photography Styles
Trisha Singhal, Junhua Liu, Lucienne T. M. Blessing, Kwan Hui Lim

TL;DR
This paper introduces 'Photozilla', a large-scale photography dataset with 990,000 images across 10 styles, and develops models for style classification and adaptation to new styles with minimal samples.
Contribution
The paper presents a new extensive dataset for photographic styles and a Siamese network approach for classifying and adapting to emerging styles with few samples.
Findings
Achieved ~96% accuracy in style classification.
Developed a Siamese network that identifies new styles with over 68% accuracy using only 25 samples.
Demonstrated the dataset's utility for style recognition and adaptation.
Abstract
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…
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Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Visual Attention and Saliency Detection · Image Retrieval and Classification Techniques
