# Object Recognition as Classification via Visual Properties

arXiv: https://arxiv.org/abs/2112.10531 · HTML: https://mlsanity.com/paper/2112.10531/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 28 references that are in the map.
- First posted on arXiv 20 Dec 2021.

## Builds on (most cited first, 3 of 3)

- [Towards Open World Recognition](https://mlsanity.com/paper/1412.5687/) (2014)
- [Continual egocentric object recognition](https://mlsanity.com/paper/1912.05029/) (2019)
- [Towards Visual Semantics](https://mlsanity.com/paper/2104.12379/) (2021)

## Built on by (most cited first, 5 of 5)

- [Aligning Visual and Lexical Semantics](https://mlsanity.com/paper/2212.06629/) (2022)
- [A Diversity-Aware Domain Development Methodology](https://mlsanity.com/paper/2208.13064/) (2022)
- [From Knowledge Representation to Knowledge Organization and Back](https://mlsanity.com/paper/2312.07302/) (2023)
- [From Knowledge Organization to Knowledge Representation and Back](https://mlsanity.com/paper/2401.11753/) (2024)
- [What can Computer Vision learn from Ranganathan?](https://mlsanity.com/paper/2601.22634/) (2026)

## Abstract (arXiv)

We base our work on the teleosemantic modelling of concepts as abilities implementing the distinct functions of recognition and classification. Accordingly, we model two types of concepts - substance concepts suited for object recognition exploiting visual properties, and classification concepts suited for classification of substance concepts exploiting linguistically grounded properties. The goal in this paper is to demonstrate that object recognition can be construed as classification via visual properties, as distinct from work in mainstream computer vision. Towards that, we present an object recognition process based on Ranganathan's four-phased faceted knowledge organization process, grounded in the teleosemantic distinctions of substance concept and classification concept. We also briefly introduce the ongoing project MultiMedia UKC, whose aim is to build an object recognition resource following our proposed process