Object Classification of Aerial Images With Bag-of-Visual Words

By Sheng Xu, Tao Fang, Deren Li, Shiwei Wang. 2010

190 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology, Remote Sensing

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. Junwei Han
2. Gong Cheng
3. Xiaoqiang Lu
4. Liangpei Zhang
5. Peicheng Zhou
6. Yanfei Zhong
7. Lei Guo
8. Yuan Yuan

In Media Technology

1. Gong Cheng
2. Junwei Han
3. Xiaoqiang Lu
4. Liangpei Zhang
5. Peicheng Zhou
6. Yanfei Zhong
7. Lei Guo
8. Yuan Yuan

In Remote Sensing

1. Yanfei Zhong
2. Gong Cheng
3. Liangpei Zhang
4. Junwei Han
5. Peicheng Zhou
6. E. Aptoula
7. Lei Guo
8. Bei Zhao

Citations in the corpus

• Remote Sensing Image Classification: No Features, No Clustering (2015, background): “nity for image annotation [16], object classification [17], target detection [18], and land use classification [19] and it has already proven its discrimination power in image classification.”
• Remote Sensing Image Retrieval With Global Morphological Texture Descriptors (2014, background): “[16] have combined spectral features with the power of grayscale co-occurrence matrices.”
• Learning Rotation-Invariant Convolutional Neural Networks for Object Detection in VHR Optical Remote Sensing Images (2016, methodology): “good performance for geospatial object detection [8], [12], [13], [17], [31].”
• Object Detection in Optical Remote Sensing Images Based on Weakly Supervised Learning and High-Level Feature Learning (2015, unknown): “We also compared the proposed WSL approach with several existing supervised learning based object detection methods including a baseline method from Xu [26], and Han ’ s method [3].”
• High-Resolution Remote-Sensing Image Classification via an Approximate Earth Mover's Distance-Based Bag-of-Features Model (2013, methodology): “In [2]–[4], the BOF model was applied for classification in land use/cover mapping of remote-sensing images.”
• Local structure learning in high resolution remote sensing image retrieval (2016, background): “So, compared to the statistical information of raw pixels, the information of these features is more robust [17] [18] [19].”
• Efficient, simultaneous detection of multi-class geospatial targets based on visual saliency modeling and discriminative learning of sparse coding (2014, methodology): “Xu et al. (2010) applied the visual Bag-of-Words (BoW) model to describe and classify geospatial objects.”
• Object Detection in High-Resolution Remote Sensing Images Using Rotation Invariant Parts Based Model (2014, methodology): “For example, in traditional detection models, such as the bag-of-word model [1], [2] and color-enhanced rotation-invariant Hough forest method [3], the structure information has not been well explored to represent the object configuration.”
• 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): “Most previous works [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] mainly focus on classifying pixels or objects (or rather, grouping of local homogeneous pixels) into their thematic class by extracting low-level image features (e.g. texture feature [9] [10], color feature [8], spatial and…”
• Semantic Classification of High-Resolution Remote-Sensing Images Based on Mid-level Features (2016, background): “Xu [11] introduced BOV representation into object classification of aerial images and demonstrated that it yielded better classification performance than LLF.”
• The Fisher Kernel Coding Framework for High Spatial Resolution Scene Classification (2016, methodology): “The BOVW scene classification method then employs the statistical histogram of the visual words in the image to describe the image, and classifies it by a non-linear support vector machine (SVM) classifier [14,26].”