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.
main_competitors.py
- This file provides the implementation of the KMedoids technique, used as baseline.
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