File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1016/j.cviu.2007.07.005
- Scopus: eid_2-s2.0-41949137770
- WOS: WOS:000255322900004
- Find via
Supplementary
- Citations:
- Appears in Collections:
Article: Unsupervised segmentation of natural images via lossy data compression
Title | Unsupervised segmentation of natural images via lossy data compression |
---|---|
Authors | |
Keywords | Clustering Image segmentation Lossy compression Mixture of Gaussian distributions Texture segmentation |
Issue Date | 2008 |
Citation | Computer Vision and Image Understanding, 2008, v. 110, n. 2, p. 212-225 How to Cite? |
Abstract | In this paper, we cast natural-image segmentation as a problem of clustering texture features as multivariate mixed data. We model the distribution of the texture features using a mixture of Gaussian distributions. Unlike most existing clustering methods, we allow the mixture components to be degenerate or nearly-degenerate. We contend that this assumption is particularly important for mid-level image segmentation, where degeneracy is typically introduced by using a common feature representation for different textures in an image. We show that such a mixture distribution can be effectively segmented by a simple agglomerative clustering algorithm derived from a lossy data compression approach. Using either 2D texture filter banks or simple fixed-size windows to obtain texture features, the algorithm effectively segments an image by minimizing the overall coding length of the feature vectors. We conduct comprehensive experiments to measure the performance of the algorithm in terms of visual evaluation and a variety of quantitative indices for image segmentation. The algorithm compares favorably against other well-known image-segmentation methods on the Berkeley image database. © 2007 Elsevier Inc. All rights reserved. |
Persistent Identifier | http://hdl.handle.net/10722/326740 |
ISSN | 2023 Impact Factor: 4.3 2023 SCImago Journal Rankings: 1.420 |
ISI Accession Number ID |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yang, Allen Y. | - |
dc.contributor.author | Wright, John | - |
dc.contributor.author | Ma, Yi | - |
dc.contributor.author | Sastry, S. Shankar | - |
dc.date.accessioned | 2023-03-31T05:26:11Z | - |
dc.date.available | 2023-03-31T05:26:11Z | - |
dc.date.issued | 2008 | - |
dc.identifier.citation | Computer Vision and Image Understanding, 2008, v. 110, n. 2, p. 212-225 | - |
dc.identifier.issn | 1077-3142 | - |
dc.identifier.uri | http://hdl.handle.net/10722/326740 | - |
dc.description.abstract | In this paper, we cast natural-image segmentation as a problem of clustering texture features as multivariate mixed data. We model the distribution of the texture features using a mixture of Gaussian distributions. Unlike most existing clustering methods, we allow the mixture components to be degenerate or nearly-degenerate. We contend that this assumption is particularly important for mid-level image segmentation, where degeneracy is typically introduced by using a common feature representation for different textures in an image. We show that such a mixture distribution can be effectively segmented by a simple agglomerative clustering algorithm derived from a lossy data compression approach. Using either 2D texture filter banks or simple fixed-size windows to obtain texture features, the algorithm effectively segments an image by minimizing the overall coding length of the feature vectors. We conduct comprehensive experiments to measure the performance of the algorithm in terms of visual evaluation and a variety of quantitative indices for image segmentation. The algorithm compares favorably against other well-known image-segmentation methods on the Berkeley image database. © 2007 Elsevier Inc. All rights reserved. | - |
dc.language | eng | - |
dc.relation.ispartof | Computer Vision and Image Understanding | - |
dc.subject | Clustering | - |
dc.subject | Image segmentation | - |
dc.subject | Lossy compression | - |
dc.subject | Mixture of Gaussian distributions | - |
dc.subject | Texture segmentation | - |
dc.title | Unsupervised segmentation of natural images via lossy data compression | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1016/j.cviu.2007.07.005 | - |
dc.identifier.scopus | eid_2-s2.0-41949137770 | - |
dc.identifier.volume | 110 | - |
dc.identifier.issue | 2 | - |
dc.identifier.spage | 212 | - |
dc.identifier.epage | 225 | - |
dc.identifier.eissn | 1090-235X | - |
dc.identifier.isi | WOS:000255322900004 | - |