This repository refers to the article Semantic Image Collection Summarization with Frequent Subgraph Mining.
Authors: Andrea Pasini, Elena Baralis, Politecnico di Torino.
Description of the program entry points:
main_position_classifier.py
- Train/validate the relative-position classifier on our position dataset
main_PRS.py:
- Build scene graphs (with object positions) for COCO (train, val and panoptic predictions)
- Generate the Pairwise Relationship Summary (PRS) from scene graphs
main_SGS.py
- Apply frequent subgraph mining to the scene graphs, to derive the Scene Graph Summary (SGS)
- Reproduce the different experimental configuration provided in our white paper
- Show frequent graphs with charts
main_sims.py
- The complete SImS pipeline (designed for COCO, but with minor changes can be applied to other datasets), including scene graph computation, PRS and SGS building.
Our labeled COCO subset for training the relative position classifier and the generated summaries can be found at: https://drive.google.com/file/d/1qZNZyAgGWkUrzFrpZaOn9-tEYWZKPo-u/view?usp=sharing