Superpixel segmentation using Linear Spectral Clustering

By Zhengqin Li, Jiansheng Chen. Computer Vision and Pattern Recognition, 2015

509 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/badd3510a24f0a6509efd979d37220fad7369c07

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. Luc Van Gool
2. Ming-Hsuan Yang
3. Dacheng Tao
4. Yi Yang
5. Xiaogang Wang
6. Andreas Geiger
7. Andrew Zisserman
8. Jitendra Malik

In Media Technology

1. Andreas Geiger
2. David Stutz
3. Joel Janai
4. Aseem Behl
5. Fatma Güney
6. Qichao Liu
7. Tao Lei
8. Zhihui Wei

Citations in the corpus

• Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art (2017, methodology): “Superpixel-based representations are obtained by a segmentation of the image into atomic regions which are ideally similar in color and texture, and respect image boundaries (Ren & Malik (2003); Achanta et al. (2012); Li & Chen (2015)).”
• Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes (2017, methodology): “In particular, we segment each image into 100 superpixels using linear spectral clustering [33].”
• Superpixels and Polygons Using Simple Non-iterative Clustering (2017, methodology): “We compare SNIC to the state-of-the-art in superpixel segmentation [13] [16] [17] [18] [28] [30] and the polygonal partitioning to the corresponding state-of-the-art [12].”
• Superpixels: An evaluation of the state-of-the-art (2016, background): “Reference (Google Scholar Citations) Color Li and Chen [77], 2015 (2)”
• CNN-Enhanced Graph Convolutional Network With Pixel- and Superpixel-Level Feature Fusion for Hyperspectral Image Classification (2020, methodology): “In this section, we test several state-of-the-art superpixel segmentation methods on the CEGCN, including the SLIC [45], the quick shift (QS) [52], the Felzenszwalb and Huttenlocher’s (FH) method [53], the linear spectral clustering (LSC) [54], and the SEEDS [55].”
• Significantly Fast and Robust Fuzzy C-Means Clustering Algorithm Based on Morphological Reconstruction and Membership Filtering (2018, methodology): “The technologies of image segmentation involve clustering [7], [8], region growth [9], watershed transform [10], active contour model [11], MeanShift [12], Graph Cut [13], spectral clustering [14], Markov random field [15], neural network [16], etc.”
• Domain Adaptation for Semantic Segmentation of Urban Scenes (2017, methodology): “In particular, we segment each image into 100 superpixels using linear spectral clustering [33].”