Instance-level Semantic Segmentation by Deep Normalized Cuts
(2017) In Master's Theses in Mathematical Sciences FMA820 20162Mathematics (Faculty of Engineering)
- Abstract
- Instance-level semantic segmentation refers to the task of assigning each pixel in an image an object class and an instance identity label. Due to its complexity, this problem has received little attention by the research community in the past. However, owing to the recent successes of deep learning technology, solving it suddenly seems possible. Starting from an initial pixel-level semantic segmentation, we combine ideas from spectral clustering and deep learning to investigate the suitability of using normalized cuts for additional instance-level segmentation. Specifically, the similarity matrix on which cuts are based is learned from examples using a convolutional network. We use the Cityscapes dataset for urban street scene... (More)
- Instance-level semantic segmentation refers to the task of assigning each pixel in an image an object class and an instance identity label. Due to its complexity, this problem has received little attention by the research community in the past. However, owing to the recent successes of deep learning technology, solving it suddenly seems possible. Starting from an initial pixel-level semantic segmentation, we combine ideas from spectral clustering and deep learning to investigate the suitability of using normalized cuts for additional instance-level segmentation. Specifically, the similarity matrix on which cuts are based is learned from examples using a convolutional network. We use the Cityscapes dataset for urban street scene understanding to train our deep normalized cuts model to segment individual cars. When using the ground truth number of instances to help determine the correct number of cuts, our method attains almost twice the performance of our connected components baseline. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8925933
- author
- Davidson, Alexander LU
- supervisor
- organization
- course
- FMA820 20162
- year
- 2017
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- Deep learning, Machine learning, Convolutional neural networks, Normalized cuts, Spectral clustering, Instance-level semantic segmentation
- publication/series
- Master's Theses in Mathematical Sciences
- report number
- LUTFMA-3329-2017
- ISSN
- 1404-6342
- other publication id
- 2017:E55
- language
- English
- id
- 8925933
- date added to LUP
- 2017-10-25 15:46:00
- date last changed
- 2017-10-25 15:46:00
@misc{8925933, abstract = {{Instance-level semantic segmentation refers to the task of assigning each pixel in an image an object class and an instance identity label. Due to its complexity, this problem has received little attention by the research community in the past. However, owing to the recent successes of deep learning technology, solving it suddenly seems possible. Starting from an initial pixel-level semantic segmentation, we combine ideas from spectral clustering and deep learning to investigate the suitability of using normalized cuts for additional instance-level segmentation. Specifically, the similarity matrix on which cuts are based is learned from examples using a convolutional network. We use the Cityscapes dataset for urban street scene understanding to train our deep normalized cuts model to segment individual cars. When using the ground truth number of instances to help determine the correct number of cuts, our method attains almost twice the performance of our connected components baseline.}}, author = {{Davidson, Alexander}}, issn = {{1404-6342}}, language = {{eng}}, note = {{Student Paper}}, series = {{Master's Theses in Mathematical Sciences}}, title = {{Instance-level Semantic Segmentation by Deep Normalized Cuts}}, year = {{2017}}, }