# Feature Evaluation of Deep Convolutional Neural Networks for Object Recognition and Detection

arXiv: https://arxiv.org/abs/1509.07627 · HTML: https://mlsanity.com/paper/1509.07627/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2015.

## Built on by (most cited first, 2 of 2)

- [A novel approach to the classification of terrestrial drainage networks based on deep learning and preliminary results on Solar System bodies](https://mlsanity.com/paper/2103.04116/) (2021)
- [Deep learning for identification and face, gender, expression recognition under constraints](https://mlsanity.com/paper/2111.01930/) (2021)

## Abstract (arXiv)

In this paper, we evaluate convolutional neural network (CNN) features using the AlexNet architecture and very deep convolutional network (VGGNet) architecture. To date, most CNN researchers have employed the last layers before output, which were extracted from the fully connected feature layers. However, since it is unlikely that feature representation effectiveness is dependent on the problem, this study evaluates additional convolutional layers that are adjacent to fully connected layers, in addition to executing simple tuning for feature concatenation (e.g., layer 3 + layer 5 + layer 7) and transformation, using tools such as principal component analysis. In our experiments, we carried out detection and classification tasks using the Caltech 101 and Daimler Pedestrian Benchmark Datasets.