Texture classification using sparse frame based representations
Konferanse fra NORSIG 2002, Tromsø / Trondheim, Norway, Oct. 4-7, 2002 In this paper a new method for texture classification, denoted Frame Texture Classification Method (FTCM), is presented. The main idea is that a frame trained to make a sparse representation of a certain class of signals is a mod...
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ftunivstavanger:oai:uis.brage.unit.no:11250/181616 2024-05-19T07:49:33+00:00 Texture classification using sparse frame based representations Skretting, Karl Husøy, John Håkon 2008-06-03T09:34:19Z 301304 bytes application/pdf http://hdl.handle.net/11250/181616 eng eng http://hdl.handle.net/11250/181616 VDP::Mathematics and natural science: 400::Information and communication science: 420 Conference object 2008 ftunivstavanger 2024-04-23T23:55:28Z Konferanse fra NORSIG 2002, Tromsø / Trondheim, Norway, Oct. 4-7, 2002 In this paper a new method for texture classification, denoted Frame Texture Classification Method (FTCM), is presented. The main idea is that a frame trained to make a sparse representation of a certain class of signals is a model for this signal class. The signal class is given by many representative image blocks of the class. Frames are trained for several textures, one frame for each texture class. A pixel of an image is classified by processing a block around the pixel, the block size is the same as the one used in the training set. Many sparse representations of this test block are found, using each of the frames trained for the texture classes under consideration. Since the frames were trained to minimize the representation error, the tested pixel is assumed to belong to the texture for which the corresponding frame has the smallest representation error. The FTCM is applied to nine test images, yielding excellent overall performance, for many test images the number of wrongly classified pixels is more than halved, in comparison to state of the art texture classification methods presented in [1]. Conference Object Tromsø University of Stavanger: UiS Brage |
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University of Stavanger: UiS Brage |
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English |
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VDP::Mathematics and natural science: 400::Information and communication science: 420 |
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VDP::Mathematics and natural science: 400::Information and communication science: 420 Skretting, Karl Husøy, John Håkon Texture classification using sparse frame based representations |
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VDP::Mathematics and natural science: 400::Information and communication science: 420 |
description |
Konferanse fra NORSIG 2002, Tromsø / Trondheim, Norway, Oct. 4-7, 2002 In this paper a new method for texture classification, denoted Frame Texture Classification Method (FTCM), is presented. The main idea is that a frame trained to make a sparse representation of a certain class of signals is a model for this signal class. The signal class is given by many representative image blocks of the class. Frames are trained for several textures, one frame for each texture class. A pixel of an image is classified by processing a block around the pixel, the block size is the same as the one used in the training set. Many sparse representations of this test block are found, using each of the frames trained for the texture classes under consideration. Since the frames were trained to minimize the representation error, the tested pixel is assumed to belong to the texture for which the corresponding frame has the smallest representation error. The FTCM is applied to nine test images, yielding excellent overall performance, for many test images the number of wrongly classified pixels is more than halved, in comparison to state of the art texture classification methods presented in [1]. |
format |
Conference Object |
author |
Skretting, Karl Husøy, John Håkon |
author_facet |
Skretting, Karl Husøy, John Håkon |
author_sort |
Skretting, Karl |
title |
Texture classification using sparse frame based representations |
title_short |
Texture classification using sparse frame based representations |
title_full |
Texture classification using sparse frame based representations |
title_fullStr |
Texture classification using sparse frame based representations |
title_full_unstemmed |
Texture classification using sparse frame based representations |
title_sort |
texture classification using sparse frame based representations |
publishDate |
2008 |
url |
http://hdl.handle.net/11250/181616 |
genre |
Tromsø |
genre_facet |
Tromsø |
op_relation |
http://hdl.handle.net/11250/181616 |
_version_ |
1799468053805662208 |