Learning Rotation-Invariant and Fisher Discriminative Convolutional Neural Networks for Object Detection
| Autor: | Gong Cheng, Peicheng Zhou, Junwei Han, Dong Xu |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry Feature extraction Pattern recognition 02 engineering and technology Pascal (programming language) Computer Graphics and Computer-Aided Design Convolutional neural network Object detection Data set Discriminative model 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Invariant (mathematics) business computer Software Aerial image computer.programming_language |
| Zdroj: | IEEE Transactions on Image Processing. 28:265-278 |
| ISSN: | 1941-0042 1057-7149 |
| DOI: | 10.1109/tip.2018.2867198 |
| Popis: | The performance of object detection has recently been significantly improved due to the powerful features learnt through convolutional neural networks (CNNs). Despite the remarkable success, there are still several major challenges in object detection, including object rotation, within-class diversity, and between-class similarity, which generally degenerate object detection performance. To address these issues, we build up the existing state-of-the-art object detection systems and propose a simple but effective method to train rotation-invariant and Fisher discriminative CNN models to further boost object detection performance. This is achieved by optimizing a new objective function that explicitly imposes a rotation-invariant regularizer and a Fisher discrimination regularizer on the CNN features. Specifically, the first regularizer enforces the CNN feature representations of the training samples before and after rotation to be mapped closely to each other in order to achieve rotation-invariance. The second regularizer constrains the CNN features to have small within-class scatter but large between-class separation. We implement our proposed method under four popular object detection frameworks, including region-CNN (R-CNN), Fast R- CNN, Faster R- CNN, and R- FCN. In the experiments, we comprehensively evaluate the proposed method on the PASCAL VOC 2007 and 2012 data sets and a publicly available aerial image data set. Our proposed methods outperform the existing baseline methods and achieve the state-of-the-art results. |
| Databáze: | OpenAIRE |
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