# ResFeats: Residual Network Based Features for Image Classification

arXiv: https://arxiv.org/abs/1611.06656 · HTML: https://mlsanity.com/paper/1611.06656/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Nov 2016.

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

- [A Discriminative Vectorial Framework for Multi-modal Feature Representation](https://mlsanity.com/paper/2103.05597/) (2021)
- [White blood cell subtype detection and classification](https://mlsanity.com/paper/2108.04614/) (2021)
- [ODMTCNet: An Interpretable Multi-view Deep Neural Network Architecture for Image Feature Representation](https://mlsanity.com/paper/2110.14830/) (2021)
- [Less yet robust: crucial region selection for scene recognition](https://mlsanity.com/paper/2409.14741/) (2024)
- [Grad-CAM for Visualizing Attention Regions of PCA and SVM Layers in Convolutional Neural Networks](https://mlsanity.com/paper/2508.11880/) (2025)

## Abstract (arXiv)

Deep residual networks have recently emerged as the state-of-the-art architecture in image segmentation and object detection. In this paper, we propose new image features (called ResFeats) extracted from the last convolutional layer of deep residual networks pre-trained on ImageNet. We propose to use ResFeats for diverse image classification tasks namely, object classification, scene classification and coral classification and show that ResFeats consistently perform better than their CNN counterparts on these classification tasks. Since the ResFeats are large feature vectors, we propose to use PCA for dimensionality reduction. Experimental results are provided to show the effectiveness of ResFeats with state-of-the-art classification accuracies on Caltech-101, Caltech-256 and MLC datasets and a significant performance improvement on MIT-67 dataset compared to the widely used CNN features.