# Deep Neural Networks Fused with Textures for Image Classification

arXiv: https://arxiv.org/abs/2308.01813 · HTML: https://mlsanity.com/paper/2308.01813/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 48 references that are in the map.
- First posted on arXiv 3 Aug 2023.

## Builds on (most cited first, 15 of 15)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Random Erasing Data Augmentation](https://mlsanity.com/paper/1708.04896/) (2017)
- [Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1710.05006/) (2017)
- [Weakly Supervised Complementary Parts Models for Fine-Grained Image Classification from the Bottom Up](https://mlsanity.com/paper/1903.02827/) (2019)
- [A Survey on Food Computing](https://mlsanity.com/paper/1808.07202/) (2018)
- [Local Binary Convolutional Neural Networks](https://mlsanity.com/paper/1608.06049/) (2016)
- [Context-aware Attentional Pooling (CAP) for Fine-grained Visual Classification](https://mlsanity.com/paper/2101.06635/) (2021)
- [Skin Lesion Analysis Towards Melanoma Detection Using Deep Learning Network](https://mlsanity.com/paper/1703.00577/) (2017)
- [A survey, review, and future trends of skin lesion segmentation and classification](https://mlsanity.com/paper/2208.12232/) (2022)
- [Deep Learning for Fine-Grained Image Analysis: A Survey](https://mlsanity.com/paper/1907.03069/) (2019)
- [FoodNet: Recognizing Foods Using Ensemble of Deep Networks](https://mlsanity.com/paper/1709.09429/) (2017)
- [Attend and Guide (AG-Net): A Keypoints-driven Attention-based Deep Network for Image Recognition](https://mlsanity.com/paper/2110.12183/) (2021)
- [Fine-Grained Sports, Yoga, and Dance Postures Recognition: A Benchmark Analysis](https://mlsanity.com/paper/2308.00323/) (2023)
- [Two decades of local binary patterns: A survey](https://mlsanity.com/paper/1612.06795/) (2016)
- [Spoofing Detection on Hand Images Using Quality Assessment](https://mlsanity.com/paper/2110.12923/) (2021)

## Built on by (most cited first, 1 of 1)

- [A self-supervised learning approach to deep filter banks for texture recognition](https://mlsanity.com/paper/2605.27843/) (2026)

## Abstract (arXiv)

Fine-grained image classification (FGIC) is a challenging task in computer vision for due to small visual differences among inter-subcategories, but, large intra-class variations. Deep learning methods have achieved remarkable success in solving FGIC. In this paper, we propose a fusion approach to address FGIC by combining global texture with local patch-based information. The first pipeline extracts deep features from various fixed-size non-overlapping patches and encodes features by sequential modelling using the long short-term memory (LSTM). Another path computes image-level textures at multiple scales using the local binary patterns (LBP). The advantages of both streams are integrated to represent an efficient feature vector for image classification. The method is tested on eight datasets representing the human faces, skin lesions, food dishes, marine lives, etc. using four standard backbone CNNs. Our method has attained better classification accuracy over existing methods with notable margins.