Mining Mid-level Features for Image Classification

By Basura Fernando, Élisa Fromont, Tinne Tuytelaars. 2014

86 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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. Liangpei Zhang
2. Gong Cheng
3. Junwei Han
4. Xiaoqiang Lu
5. Gui-Song Xia
6. Fan Hu
7. Bo Du
8. Yanfei Zhong

Citations in the corpus

• Remote Sensing Image Scene Classification: Benchmark and State of the Art (2017, methodology): “dataset is the most popular and has been widely used for the task of remote sensing image scene classification and retrieval [8, 10, 11, 14-16, 18-20, 24, 26, 28-30, 36, 38, 53, 55, 59, 61, 62, 81, 82, 84-98].”
• Scene classification using multi-scale deeply described visual words (2016, unknown): “To date, several scene classi fi cation algorithms, aiming at automatically labelling images, have been proposed and have shown remarkable success in image interpretation (Fernando, Fromont, and Tuytelaars 2014; Luo et al. 2005; Zhou, Zhou, and Hu 2013).”
• Strathprints Institutional Repository Shuhui and Ren, Jinchang (2015) Effective and Efficient Midlevel Visual Elements-oriented Land-use Classification Using Vhr Remote Sensing Images. Ieee Transactions on Geoscience and Remote Sensing, 53 (8). Pp. 4238-4249. Issn 0196-2892 Effective and Efficient M (unknown): “…on pixel or object-level thematic classification, the scene-level land-use classification using very high resolution (VHR) remote sensing images has attracted more and more attentions [3] [4] [5] [6] [7] [19] [20] because of its foundation and importance in automated image content understanding.”
• Unsupervised Feature Learning Via Spectral Clustering of Multidimensional Patches for Remotely Sensed Scene Classification (2015, methodology): “The BOW methods have shown impressive performance in scene classification and other tasks [39]–[42].”
• Semantic Classification of High-Resolution Remote-Sensing Images Based on Mid-level Features (2016, methodology): “To further demonstrate the superiority of the mid-level representation, multifeature joint sparse coding (MFJSC) [27], grid-specified frequent local histograms (GRID-FLH) [28], and partlets-based method [29] are compared to MS-BOV.”