# Image Classification with Classic and Deep Learning Techniques

arXiv: https://arxiv.org/abs/2105.04895 · HTML: https://mlsanity.com/paper/2105.04895/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 14 references that are in the map.
- First posted on arXiv 11 May 2021.

## Builds on (most cited first, 6 of 6)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay](https://mlsanity.com/paper/1803.09820/) (2018)

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

- [Interpretable Computer Vision Models through Adversarial Training: Unveiling the Robustness-Interpretability Connection](https://mlsanity.com/paper/2307.02500/) (2023)
- [Pengembangan Model untuk Mendeteksi Kerusakan pada Terumbu Karang dengan Klasifikasi Citra](https://mlsanity.com/paper/2308.04337/) (2023)
- [Assistive Image Annotation Systems with Deep Learning and Natural Language Capabilities: A Review](https://mlsanity.com/paper/2407.00252/) (2024)
- [A Multi-Level Hierarchical Framework for the Classification of Weather Conditions and Hazard Prediction](https://mlsanity.com/paper/2407.16834/) (2024)

## Abstract (arXiv)

To classify images based on their content is one of the most studied topics in the field of computer vision. Nowadays, this problem can be addressed using modern techniques such as Convolutional Neural Networks (CNN), but over the years different classical methods have been developed. In this report, we implement an image classifier using both classic computer vision and deep learning techniques. Specifically, we study the performance of a Bag of Visual Words classifier using Support Vector Machines, a Multilayer Perceptron, an existing architecture named InceptionV3 and our own CNN, TinyNet, designed from scratch. We evaluate each of the cases in terms of accuracy and loss, and we obtain results that vary between 0.6 and 0.96 depending on the model and configuration used.