Compact Dominant Synergistic Excitation Pattern Learning for Illumination-Insensitive Image Representation With Boosting
Illumination-insensitive image representation is a great challenge in the computer vision field. Illumination variations considerably obstruct the effectiveness of image feature extraction. In this paper, we present a novel and generalized learning framework for illumination-insensitive image repres...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8674724/ |
| _version_ | 1819298247981662208 |
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| author | Tao Gao Ting Chen C. C. Wang Zhanwen Liu Wei Lu Y. H. Li |
| author_facet | Tao Gao Ting Chen C. C. Wang Zhanwen Liu Wei Lu Y. H. Li |
| author_sort | Tao Gao |
| collection | DOAJ |
| description | Illumination-insensitive image representation is a great challenge in the computer vision field. Illumination variations considerably obstruct the effectiveness of image feature extraction. In this paper, we present a novel and generalized learning framework for illumination-insensitive image representation, which can learn the discriminative features through maximizing the inter-difference and minimizing intra-difference of the images with boosting. Particularly, we enhance the discriminative capacity of illumination-insensitive image representation in three aspects. First, we learn a subset of different synergistic Weber excitation patterns (SWEP) to generate the dominant SWEP (DSWEP) and DSWEP codebook for exploring optimal illumination-insensitive patterns. Second, a compact DSWEP (C-DSWEP) is learned with a boosted set of weight to generate C-DSWEP codebook. Discriminative learning is aimed at robustness and compactness. Third, the discriminative histogram learning model is established for encoding CDSEP to further improve the discriminative ability and reduce redundancy. The extensive experiments on CMUPIE, FERET, Yale B, Yale B ext., LFW, and PhoTex databases have highlighted the superiority and the robustness of our method compared with some other state-of-the-art methods. |
| first_indexed | 2024-12-24T05:26:52Z |
| format | Article |
| id | doaj.art-95a809ede7db4c8788ee64bdd25dfe25 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-24T05:26:52Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-95a809ede7db4c8788ee64bdd25dfe252022-12-21T17:13:17ZengIEEEIEEE Access2169-35362019-01-017407464075610.1109/ACCESS.2019.29038498674724Compact Dominant Synergistic Excitation Pattern Learning for Illumination-Insensitive Image Representation With BoostingTao Gao0https://orcid.org/0000-0003-0325-7912Ting Chen1https://orcid.org/0000-0002-8134-6913C. C. Wang2Zhanwen Liu3Wei Lu4Y. H. Li5School of Information Engineering, Chang’an University, Xi’an, ChinaSchool of Information Engineering, Chang’an University, Xi’an, ChinaSchool of Information Engineering, Chang’an University, Xi’an, ChinaSchool of Information Engineering, Chang’an University, Xi’an, ChinaSchool of Information Engineering, Chang’an University, Xi’an, ChinaSchool of Information Engineering, Chang’an University, Xi’an, ChinaIllumination-insensitive image representation is a great challenge in the computer vision field. Illumination variations considerably obstruct the effectiveness of image feature extraction. In this paper, we present a novel and generalized learning framework for illumination-insensitive image representation, which can learn the discriminative features through maximizing the inter-difference and minimizing intra-difference of the images with boosting. Particularly, we enhance the discriminative capacity of illumination-insensitive image representation in three aspects. First, we learn a subset of different synergistic Weber excitation patterns (SWEP) to generate the dominant SWEP (DSWEP) and DSWEP codebook for exploring optimal illumination-insensitive patterns. Second, a compact DSWEP (C-DSWEP) is learned with a boosted set of weight to generate C-DSWEP codebook. Discriminative learning is aimed at robustness and compactness. Third, the discriminative histogram learning model is established for encoding CDSEP to further improve the discriminative ability and reduce redundancy. The extensive experiments on CMUPIE, FERET, Yale B, Yale B ext., LFW, and PhoTex databases have highlighted the superiority and the robustness of our method compared with some other state-of-the-art methods.https://ieeexplore.ieee.org/document/8674724/Feature extractionWeber lawfeature learningillumination variations |
| spellingShingle | Tao Gao Ting Chen C. C. Wang Zhanwen Liu Wei Lu Y. H. Li Compact Dominant Synergistic Excitation Pattern Learning for Illumination-Insensitive Image Representation With Boosting IEEE Access Feature extraction Weber law feature learning illumination variations |
| title | Compact Dominant Synergistic Excitation Pattern Learning for Illumination-Insensitive Image Representation With Boosting |
| title_full | Compact Dominant Synergistic Excitation Pattern Learning for Illumination-Insensitive Image Representation With Boosting |
| title_fullStr | Compact Dominant Synergistic Excitation Pattern Learning for Illumination-Insensitive Image Representation With Boosting |
| title_full_unstemmed | Compact Dominant Synergistic Excitation Pattern Learning for Illumination-Insensitive Image Representation With Boosting |
| title_short | Compact Dominant Synergistic Excitation Pattern Learning for Illumination-Insensitive Image Representation With Boosting |
| title_sort | compact dominant synergistic excitation pattern learning for illumination insensitive image representation with boosting |
| topic | Feature extraction Weber law feature learning illumination variations |
| url | https://ieeexplore.ieee.org/document/8674724/ |
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