Image segmentation using overlapping group sparsity

Shervin Minaee, Yao Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Sparse decomposition has been widely used for different applications, such as source separation, image classification and image denoising. This paper presents a new algorithm for segmentation of an image into background and foreground text and graphics using sparse decomposition. First, the background is represented using a suitable smooth model, which is a linear combination of a few smoothly varying basis functions, and the foreground text and graphics are modeled as a sparse component overlaid on the smooth background. Then the background and foreground are separated using a sparse decomposition framework and imposing some prior information, which promote the smoothness of background, and the sparsity and connectivity of foreground pixels. This algorithm has been tested on a dataset of images extracted from HEVC standard test sequences for screen content coding, and is shown to outperform prior methods, including least absolute deviation fitting, k-means clustering based segmentation in DjVu, and shape primitive extraction and coding algorithm.

Original languageEnglish (US)
Title of host publication2016 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509067138
DOIs
StatePublished - Feb 7 2017
Event2016 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2016 - Philadelphia, United States
Duration: Dec 3 2016 → …

Other

Other2016 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2016
CountryUnited States
CityPhiladelphia
Period12/3/16 → …

Fingerprint

Image segmentation
Decomposition
Source separation
Image denoising
Image classification
Pixels

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering

Cite this

Minaee, S., & Wang, Y. (2017). Image segmentation using overlapping group sparsity. In 2016 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2016 - Proceedings [7846856] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SPMB.2016.7846856

Image segmentation using overlapping group sparsity. / Minaee, Shervin; Wang, Yao.

2016 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. 7846856.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Minaee, S & Wang, Y 2017, Image segmentation using overlapping group sparsity. in 2016 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2016 - Proceedings., 7846856, Institute of Electrical and Electronics Engineers Inc., 2016 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2016, Philadelphia, United States, 12/3/16. https://doi.org/10.1109/SPMB.2016.7846856
Minaee S, Wang Y. Image segmentation using overlapping group sparsity. In 2016 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2017. 7846856 https://doi.org/10.1109/SPMB.2016.7846856
Minaee, Shervin ; Wang, Yao. / Image segmentation using overlapping group sparsity. 2016 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017.
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