# Scene learning, recognition and similarity detection in a fuzzy ontology via human examples

arXiv: https://arxiv.org/abs/1709.09433 · HTML: https://mlsanity.com/paper/1709.09433/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2017.

## Built on by (most cited first, 2 of 2)

- [Learning Symbolic Task Representation from a Human-Led Demonstration: A Memory to Store, Retrieve, Consolidate, and Forget Experiences](https://mlsanity.com/paper/2404.10591/) (2024)
- [Incremental Bootstrapping and Classification of Structured Scenes in a Fuzzy Ontology](https://mlsanity.com/paper/2404.11744/) (2024)

## Abstract (arXiv)

This paper introduces a Fuzzy Logic framework for scene learning, recognition and similarity detection, where scenes are taught via human examples. The framework allows a robot to: (i) deal with the intrinsic vagueness associated with determining spatial relations among objects; (ii) infer similarities and dissimilarities in a set of scenes, and represent them in a hierarchical structure represented in a Fuzzy ontology. In this paper, we briefly formalize our approach and we provide a few use cases by way of illustration. Nevertheless, we discuss how the framework can be used in real-world scenarios.