Scene categorization by Hessian-regularized active perceptual feature selection
Abstract Decoding the semantic categories of complex sceneries is fundamental to numerous artificial intelligence (AI) infrastructures. This work presents an advanced selection of multi-channel perceptual visual features for recognizing scenic images with elaborate spatial structures, focusing on de...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-84181-x |
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| author | Junwu Zhou Fuji Ren |
| author_facet | Junwu Zhou Fuji Ren |
| author_sort | Junwu Zhou |
| collection | DOAJ |
| description | Abstract Decoding the semantic categories of complex sceneries is fundamental to numerous artificial intelligence (AI) infrastructures. This work presents an advanced selection of multi-channel perceptual visual features for recognizing scenic images with elaborate spatial structures, focusing on developing a deep hierarchical model dedicated to learning human gaze behavior. Utilizing the BING objectness measure, we efficiently localize objects or their details across varying scales within scenes. To emulate humans observing semantically or visually significant areas within scenes, we propose a robust deep active learning (RDAL) strategy. This strategy progressively generates gaze shifting paths (GSP) and calculates deep GSP representations within a unified architecture. A notable advantage of RDAL is the robustness to label noise, which is implemented by a carefully-designed sparse penalty term. This mechanism ensures that irrelevant or misleading deep GSP features are intelligently discarded. Afterward, a novel Hessian-regularized Feature Selector (HFS) is proposed to select high-quality features from the deep GSP features, wherein (i) the spatial composition of scenic patches can be optimally maintained, and (ii) a linear SVM is learned simultaneously. Empirical evaluations across six standard scenic datasets demonstrated our method’s superior performance, highlighting its exceptional ability to differentiate various sophisticated scenery categories. |
| format | Article |
| id | doaj-art-1d9b9adcccc74b088623da4c97592036 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-1d9b9adcccc74b088623da4c975920362025-01-05T12:15:06ZengNature PortfolioScientific Reports2045-23222025-01-0115111410.1038/s41598-024-84181-xScene categorization by Hessian-regularized active perceptual feature selectionJunwu Zhou0Fuji Ren1School of Higher Vocational and Technical College, Shanghai Dianji UniversityCollege of Computer Sciences, Anhui UniversityAbstract Decoding the semantic categories of complex sceneries is fundamental to numerous artificial intelligence (AI) infrastructures. This work presents an advanced selection of multi-channel perceptual visual features for recognizing scenic images with elaborate spatial structures, focusing on developing a deep hierarchical model dedicated to learning human gaze behavior. Utilizing the BING objectness measure, we efficiently localize objects or their details across varying scales within scenes. To emulate humans observing semantically or visually significant areas within scenes, we propose a robust deep active learning (RDAL) strategy. This strategy progressively generates gaze shifting paths (GSP) and calculates deep GSP representations within a unified architecture. A notable advantage of RDAL is the robustness to label noise, which is implemented by a carefully-designed sparse penalty term. This mechanism ensures that irrelevant or misleading deep GSP features are intelligently discarded. Afterward, a novel Hessian-regularized Feature Selector (HFS) is proposed to select high-quality features from the deep GSP features, wherein (i) the spatial composition of scenic patches can be optimally maintained, and (ii) a linear SVM is learned simultaneously. Empirical evaluations across six standard scenic datasets demonstrated our method’s superior performance, highlighting its exceptional ability to differentiate various sophisticated scenery categories.https://doi.org/10.1038/s41598-024-84181-x |
| spellingShingle | Junwu Zhou Fuji Ren Scene categorization by Hessian-regularized active perceptual feature selection Scientific Reports |
| title | Scene categorization by Hessian-regularized active perceptual feature selection |
| title_full | Scene categorization by Hessian-regularized active perceptual feature selection |
| title_fullStr | Scene categorization by Hessian-regularized active perceptual feature selection |
| title_full_unstemmed | Scene categorization by Hessian-regularized active perceptual feature selection |
| title_short | Scene categorization by Hessian-regularized active perceptual feature selection |
| title_sort | scene categorization by hessian regularized active perceptual feature selection |
| url | https://doi.org/10.1038/s41598-024-84181-x |
| work_keys_str_mv | AT junwuzhou scenecategorizationbyhessianregularizedactiveperceptualfeatureselection AT fujiren scenecategorizationbyhessianregularizedactiveperceptualfeatureselection |