Feature representation for statistical-learning-based object detection: A review

By Yali Li, Shengjin Wang, Qi Tian, Xiaoqing Ding. 2015

98 citations recorded by the source corpus.

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. Qiuhong Ke
2. Ferdous Sohel
3. Farid Boussaïd
4. Mohammed Bennamoun
5. Senjian An
6. Jun Liu
7. Hanqing Lu
8. Jian Cheng

In Media Technology

1. Qiuhong Ke
2. Mohammed Bennamoun
3. Senjian An
4. Ferdous Sohel
5. Farid Boussaïd

Citations in the corpus

• Deep Learning for Generic Object Detection: A Survey (2018, unknown): “Feature Representation for Statistical Learning based Object Detection: A Review [160] 2015 PR 統計的学習ベースの物体検出における,ハンドクラフト特徴・深層学 習ベース特徴を含む特徴表現手法”
• A Survey on Object Detection in Optical Remote Sensing Images (2016, unknown): “…2015b); airport (Aytekin et al., 2013; Cheng et al., 2014c; Tao et al., 2011; Yao et al., 2015); building (Ari and Aksoy, 2014; Lei et al., 2012; Li et al., 2015a; Senaras et al., 2013; Wegner et al., 2011a; Wegner et al., 2011b); change detection (Benedek et al., 2015; Bovolo et al., 2008; De…”
• Spatial, Structural and Temporal Feature Learning for Human Interaction Prediction (2016, methodology): “As CNN (Convolutional Neural Network) features have been shown transferable across domains [6], and have been successfully applied in a variety of visual recognition tasks such as object detection, image classification and segmentation [7, 8, 9], this paper applies a CNN model, pre-trained with Imag”