Scene-Level Geographic Image Classification Based on a Covariance Descriptor Using Supervised Collaborative Kernel Coding

By Chunwei Yang, Huaping Liu, Shicheng Wang, Shouyi Liao. 2016

6 citations recorded by the source corpus.

Scene-level geographic image classification has been a very challenging problem and has become a research focus in recent years. This paper develops a supervised collaborative kernel coding method based on a covariance descriptor (covd) for scene-level geographic image classification. First, covd is introduced in the feature extraction process and, then, is transformed to a Euclidean feature by a supervised collaborative kernel coding model. Furthermore, we develop an iterative optimization framework to solve this model. Comprehensive evaluations on public high-resolution aerial image dataset and comparisons with state-of-the-art methods show the superiority and effectiveness of our approach.

Subfields: Computer Vision and Pattern Recognition, Media Technology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Wenzhi Zhao
2. Shihong Du
3. Shutao Li
4. Pedram Ghamisi
5. Jon Atli Benediktsson
6. Heesung Kwon
7. Hyungtae Lee

In Media Technology

1. Shihong Du
2. Wenzhi Zhao
3. Shutao Li
4. Pedram Ghamisi
5. Bertrand Le Saux
6. Sébastien Lefèvre
7. Nicolas Audebert
8. Jingxiang Yang

Citations in the corpus

• Scene classification using multi-scale deeply described visual words (2016, background): “Scene classi fi cation (Boutell et al. 2004; Yang et al. 2016) is a bridge to link low-level image information with the semantic content of images.”