Classification of Painting Cracks for Content-based Retrieval
Classification of Painting Cracks for Content-based Retrieval
In this paper we present steps taken to implement a content-based analysis of crack patterns in paintings. Cracks are first detected using a morphological top-hat operator and grid-based automatic thresholding. From a 1-pixel wide representation of crack patterns, we generate a statistical structure of global and local features from a chain-code based representation. A well structured model of the crack patterns allows post-processing to be performed such as pruning and high-level feature extraction. High-level features are extracted from the structured model utilising information mainly based on orientation and length of line segments. Our strategy for classifying the crack patterns makes use of an unsupervised approach which incorporates fuzzy clustering of the patterns. We present results using the fuzzy k-means technique.
Feature extraction, morphological ?lters, crack detection, clustering
Abas, FS
368323dd-5a71-476d-a89b-04d8eaabc145
Martinez, K
5f711898-20fc-410e-a007-837d8c57cb18
2003
Abas, FS
368323dd-5a71-476d-a89b-04d8eaabc145
Martinez, K
5f711898-20fc-410e-a007-837d8c57cb18
(2003)
Classification of Painting Cracks for Content-based Retrieval.
Abas, FS and Martinez, K
(eds.)
IS&T/SPIE's 15th Annual Symposium Electronic Imaging 2003 : Machine Vision Applications in Industrial Inspection XI, Santa Clara, California, United States.
20 - 24 Jan 2003.
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Conference or Workshop Item
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Abstract
In this paper we present steps taken to implement a content-based analysis of crack patterns in paintings. Cracks are first detected using a morphological top-hat operator and grid-based automatic thresholding. From a 1-pixel wide representation of crack patterns, we generate a statistical structure of global and local features from a chain-code based representation. A well structured model of the crack patterns allows post-processing to be performed such as pruning and high-level feature extraction. High-level features are extracted from the structured model utilising information mainly based on orientation and length of line segments. Our strategy for classifying the crack patterns makes use of an unsupervised approach which incorporates fuzzy clustering of the patterns. We present results using the fuzzy k-means technique.
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spie_cracks.pdf
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Published date: 2003
Additional Information:
Event Dates: 20-24 January
Venue - Dates:
IS&T/SPIE's 15th Annual Symposium Electronic Imaging 2003 : Machine Vision Applications in Industrial Inspection XI, Santa Clara, California, United States, 2003-01-20 - 2003-01-24
Keywords:
Feature extraction, morphological ?lters, crack detection, clustering
Organisations:
Web & Internet Science
Identifiers
Local EPrints ID: 257294
URI: http://eprints.soton.ac.uk/id/eprint/257294
PURE UUID: 105a8c84-a95e-46e8-814f-b4d0800620f4
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Date deposited: 20 Feb 2003
Last modified: 15 Mar 2024 02:53
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Contributors
Editor:
FS Abas
Editor:
K Martinez
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