# VisGraphNet: a complex network interpretation of convolutional neural features

arXiv: https://arxiv.org/abs/2108.12490 · HTML: https://mlsanity.com/paper/2108.12490/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 50 references that are in the map.
- First posted on arXiv 27 Aug 2021.

## Builds on (most cited first, 10 of 10)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Describing Textures in the Wild](https://mlsanity.com/paper/1311.3618/) (2013)
- [DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition](https://mlsanity.com/paper/1310.1531/) (2013)
- [Multimodal Transformer with Multi-View Visual Representation for Image Captioning](https://mlsanity.com/paper/1905.07841/) (2019)
- [PCANet: A Simple Deep Learning Baseline for Image Classification?](https://mlsanity.com/paper/1404.3606/) (2014)
- [MatConvNet - Convolutional Neural Networks for MATLAB](https://mlsanity.com/paper/1412.4564/) (2014)
- [Deep filter banks for texture recognition, description, and segmentation](https://mlsanity.com/paper/1507.02620/) (2015)
- [Using Filter Banks in Convolutional Neural Networks for Texture Classification](https://mlsanity.com/paper/1601.02919/) (2016)
- [Multi-modal Face Pose Estimation with Multi-task Manifold Deep Learning](https://mlsanity.com/paper/1712.06467/) (2017)
- [Discrete Schroedinger Transform For Texture Recognition](https://mlsanity.com/paper/1612.02498/) (2016)

## Built on by (most cited first, 4 of 4)

- [Multilayer deep feature extraction for visual texture recognition](https://mlsanity.com/paper/2208.10044/) (2022)
- [Chaotic Contrastive Learning for Robust Texture Classification](https://mlsanity.com/paper/2605.05012/) (2026)
- [A self-supervised learning approach to deep filter banks for texture recognition](https://mlsanity.com/paper/2605.27843/) (2026)
- [HiPerViT: A Hierarchical Perceiver-Vision Transformer Architecture for Multi-Scale Texture Recognition](https://mlsanity.com/paper/2609.10917/) (2026)

## Abstract (arXiv)

Here we propose and investigate the use of visibility graphs to model the feature map of a neural network. The model, initially devised for studies on complex networks, is employed here for the classification of texture images. The work is motivated by an alternative viewpoint provided by these graphs over the original data. The performance of the proposed method is verified in the classification of four benchmark databases, namely, KTHTIPS-2b, FMD, UIUC, and UMD and in a practical problem, which is the identification of plant species using scanned images of their leaves. Our method was competitive with other state-of-the-art approaches, confirming the potential of techniques used for data analysis in different contexts to give more meaningful interpretation to the use of neural networks in texture classification.