Shape-based Invariant Texture Indexing
This paper introduces a new texture analysis scheme, which is invariant to local geometric and radiometric changes. The proposed methodology relies on the topographic map of images, obtained from the connected components of level sets. This morphological tool, providing a multi-scale and contrast-in...
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| Veröffentlicht in: | International journal of computer vision 2010-07, Vol.88 (3), p.382-403 |
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| container_title | International journal of computer vision |
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| creator | Xia, Gui-Song Delon, Julie Gousseau, Yann |
| description | This paper introduces a new texture analysis scheme, which is invariant to local geometric and radiometric changes. The proposed methodology relies on the topographic map of images, obtained from the connected components of level sets. This morphological tool, providing a multi-scale and contrast-invariant representation of images, is shown to be well suited to texture analysis. We first make use of invariant moments to extract geometrical information from the topographic map. This yields features that are invariant to local similarities or local affine transformations. These features are invariant to any local contrast change. We then relax this invariance by computing additional features that are invariant to local affine contrast changes and investigate the resulting analysis scheme by performing classification and retrieval experiments on three texture databases. The obtained experimental results outperform the current state of the art in locally invariant texture analysis. |
| doi_str_mv | 10.1007/s11263-009-0312-3 |
| format | Article |
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The proposed methodology relies on the topographic map of images, obtained from the connected components of level sets. This morphological tool, providing a multi-scale and contrast-invariant representation of images, is shown to be well suited to texture analysis. We first make use of invariant moments to extract geometrical information from the topographic map. This yields features that are invariant to local similarities or local affine transformations. These features are invariant to any local contrast change. We then relax this invariance by computing additional features that are invariant to local affine contrast changes and investigate the resulting analysis scheme by performing classification and retrieval experiments on three texture databases. 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The proposed methodology relies on the topographic map of images, obtained from the connected components of level sets. This morphological tool, providing a multi-scale and contrast-invariant representation of images, is shown to be well suited to texture analysis. We first make use of invariant moments to extract geometrical information from the topographic map. This yields features that are invariant to local similarities or local affine transformations. These features are invariant to any local contrast change. We then relax this invariance by computing additional features that are invariant to local affine contrast changes and investigate the resulting analysis scheme by performing classification and retrieval experiments on three texture databases. The obtained experimental results outperform the current state of the art in locally invariant texture analysis.</description><subject>Applied sciences</subject><subject>Artificial Intelligence</subject><subject>Classification</subject><subject>Computer Imaging</subject><subject>Computer Science</subject><subject>Computer science; control theory; systems</subject><subject>Computer Vision and Pattern Recognition</subject><subject>Exact sciences and technology</subject><subject>Food science</subject><subject>Image Processing</subject><subject>Image Processing and Computer Vision</subject><subject>Indexing</subject><subject>Invariants</subject><subject>Pattern Recognition</subject><subject>Pattern Recognition and Graphics</subject><subject>Pattern recognition. Digital image processing. 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| ispartof | International journal of computer vision, 2010-07, Vol.88 (3), p.382-403 |
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| subjects | Applied sciences Artificial Intelligence Classification Computer Imaging Computer Science Computer science control theory systems Computer Vision and Pattern Recognition Exact sciences and technology Food science Image Processing Image Processing and Computer Vision Indexing Invariants Pattern Recognition Pattern Recognition and Graphics Pattern recognition. Digital image processing. Computational geometry Representations Studies Surface layer Texture Topography Vision |
| title | Shape-based Invariant Texture Indexing |
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