# Deep Features for training Support Vector Machine

arXiv: https://arxiv.org/abs/2104.03488 · HTML: https://mlsanity.com/paper/2104.03488/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 83 references that are in the map.
- First posted on arXiv 8 Apr 2021.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [CNN Features off-the-shelf: an Astounding Baseline for Recognition](https://mlsanity.com/paper/1403.6382/) (2014)
- [PCANet: A Simple Deep Learning Baseline for Image Classification?](https://mlsanity.com/paper/1404.3606/) (2014)
- [Multi-scale Orderless Pooling of Deep Convolutional Activation Features](https://mlsanity.com/paper/1403.1840/) (2014)
- [Feature Representation in Convolutional Neural Networks](https://mlsanity.com/paper/1507.02313/) (2015)
- [Deep learning for Plankton and Coral Classification](https://mlsanity.com/paper/1908.05489/) (2019)
- [Deep convolutional filter banks for texture recognition and segmentation](https://mlsanity.com/paper/1411.6836/) (2014)
- [Automated identification of hookahs (waterpipes) on Instagram: an application in feature extraction using Convolutional Neural Network and Support Vector Machine classification](https://mlsanity.com/paper/1810.08881/) (2018)
- [Co-occurrence of deep convolutional features for image search](https://mlsanity.com/paper/2003.13827/) (2020)

## Built on by (most cited first, 3 of 3)

- [High performing ensemble of convolutional neural networks for insect pest image detection](https://mlsanity.com/paper/2108.12539/) (2021)
- [Feature transforms for image data augmentation](https://mlsanity.com/paper/2201.09700/) (2022)
- [Heterogeneous virus classification using a functional deep learning model based on transmission electron microscopy images (Preprint)](https://mlsanity.com/paper/2405.15563/) (2024)

## Abstract (arXiv)

Features play a crucial role in computer vision. Initially designed to detect salient elements by means of handcrafted algorithms, features are now often learned by different layers in Convolutional Neural Networks (CNNs). This paper develops a generic computer vision system based on features extracted from trained CNNs. Multiple learned features are combined into a single structure to work on different image classification tasks. The proposed system was experimentally derived by testing several approaches for extracting features from the inner layers of CNNs and using them as inputs to SVMs that are then combined by sum rule. Dimensionality reduction techniques are used to reduce the high dimensionality of inner layers. The resulting vision system is shown to significantly boost the performance of standard CNNs across a large and diverse collection of image data sets. An ensemble of different topologies using the same approach obtains state-of-the-art results on a virus data set.