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SImS (Semantic Image Summarization)

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