Object-Specific Multiview Classification Through View-Compatible Feature Fusion
Multi-view classification (MVC) typically focuses on categorizing objects into distinct classes by employing multiple perspectives of the same objects. However, in numerous real-world applications, such as industrial inspection and quality control, there is an increasing need to distinguish particul...
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MDPI AG
2025-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/13/4127 |
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| author | Javier Perez Soler Jose-Luis Guardiola Nicolás García Sastre Pau Garrigues Carbó Miguel Sanchis Hernández Juan-Carlos Perez-Cortes |
| author_facet | Javier Perez Soler Jose-Luis Guardiola Nicolás García Sastre Pau Garrigues Carbó Miguel Sanchis Hernández Juan-Carlos Perez-Cortes |
| author_sort | Javier Perez Soler |
| collection | DOAJ |
| description | Multi-view classification (MVC) typically focuses on categorizing objects into distinct classes by employing multiple perspectives of the same objects. However, in numerous real-world applications, such as industrial inspection and quality control, there is an increasing need to distinguish particular objects from a pool of similar ones while simultaneously disregarding unknown objects. In these scenarios, relying on a single image may not provide sufficient information to effectively identify the scrutinized object, as different perspectives may reveal distinct characteristics that are essential for accurate classification. Most existing approaches operate within closed-set environments and are focused on generalization, which makes them less effective in distinguishing individual objects from others. This limitations are particularly problematic in industrial quality assessment, where distinguishing between specific objects and discarding unknowns is crucial. To address this challenge, we introduce a View-Compatible Feature Fusion (VCFF) method that utilizes images from predetermined positions as an accurate solution for multi-view classification of specific objects. Unlike other approaches, VCFF explicitly integrates pose information during the fusion process. It does not merely use pose as auxiliary data but employs it to align and selectively fuse features from different views. This mathematically explicit fusion of rotations, based on relative poses, allows VCFF to effectively combine multi-view information, enhancing classification accuracy. Through experimental evaluations, we demonstrate that the proposed VCFF method outperforms state-of-the-art MVC algorithms, especially in open-set scenarios, where the set of possible objects is not fully known in advance. Remarkably, VCFF achieves an average precision of 1.0 using only 8 cameras, whereas existing methods require 20 cameras to reach a maximum of 0.95. In terms of AUC-ROC under the constraint of fewer than 3<inline-formula><math display="inline"><semantics><mi>σ</mi></semantics></math></inline-formula> false positives—a critical metric in industrial inspection—current state-of-the-art methods achieve up to 0.72, while VCFF attains a perfect score of 1.0 with just eight cameras. Furthermore, our approach delivers highly accurate rotation estimation, maintaining an error margin slightly above 2° when sampling at 4° intervals. |
| format | Article |
| id | doaj-art-d989ffdb6c424b73be8a67c0970c3942 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-d989ffdb6c424b73be8a67c0970c39422025-08-20T03:29:02ZengMDPI AGSensors1424-82202025-07-012513412710.3390/s25134127Object-Specific Multiview Classification Through View-Compatible Feature FusionJavier Perez Soler0Jose-Luis Guardiola1Nicolás García Sastre2Pau Garrigues Carbó3Miguel Sanchis Hernández4Juan-Carlos Perez-Cortes5Instituto Tecnológico de Informática (ITI), C. Nicolás Copérnico, 7, 46022 Valencia, SpainDepartamento de Informática de Sistemas y Computadores (DISCA), Universitat Politècnica de València (UPV), 46022 Valencia, SpainInstituto Tecnológico de Informática (ITI), C. Nicolás Copérnico, 7, 46022 Valencia, SpainInstituto Tecnológico de Informática (ITI), C. Nicolás Copérnico, 7, 46022 Valencia, SpainInstituto Tecnológico de Informática (ITI), C. Nicolás Copérnico, 7, 46022 Valencia, SpainDepartamento de Informática de Sistemas y Computadores (DISCA), Universitat Politècnica de València (UPV), 46022 Valencia, SpainMulti-view classification (MVC) typically focuses on categorizing objects into distinct classes by employing multiple perspectives of the same objects. However, in numerous real-world applications, such as industrial inspection and quality control, there is an increasing need to distinguish particular objects from a pool of similar ones while simultaneously disregarding unknown objects. In these scenarios, relying on a single image may not provide sufficient information to effectively identify the scrutinized object, as different perspectives may reveal distinct characteristics that are essential for accurate classification. Most existing approaches operate within closed-set environments and are focused on generalization, which makes them less effective in distinguishing individual objects from others. This limitations are particularly problematic in industrial quality assessment, where distinguishing between specific objects and discarding unknowns is crucial. To address this challenge, we introduce a View-Compatible Feature Fusion (VCFF) method that utilizes images from predetermined positions as an accurate solution for multi-view classification of specific objects. Unlike other approaches, VCFF explicitly integrates pose information during the fusion process. It does not merely use pose as auxiliary data but employs it to align and selectively fuse features from different views. This mathematically explicit fusion of rotations, based on relative poses, allows VCFF to effectively combine multi-view information, enhancing classification accuracy. Through experimental evaluations, we demonstrate that the proposed VCFF method outperforms state-of-the-art MVC algorithms, especially in open-set scenarios, where the set of possible objects is not fully known in advance. Remarkably, VCFF achieves an average precision of 1.0 using only 8 cameras, whereas existing methods require 20 cameras to reach a maximum of 0.95. In terms of AUC-ROC under the constraint of fewer than 3<inline-formula><math display="inline"><semantics><mi>σ</mi></semantics></math></inline-formula> false positives—a critical metric in industrial inspection—current state-of-the-art methods achieve up to 0.72, while VCFF attains a perfect score of 1.0 with just eight cameras. Furthermore, our approach delivers highly accurate rotation estimation, maintaining an error margin slightly above 2° when sampling at 4° intervals.https://www.mdpi.com/1424-8220/25/13/4127multi-view classificationfeature fusionindustrial inspectionopen-set classification |
| spellingShingle | Javier Perez Soler Jose-Luis Guardiola Nicolás García Sastre Pau Garrigues Carbó Miguel Sanchis Hernández Juan-Carlos Perez-Cortes Object-Specific Multiview Classification Through View-Compatible Feature Fusion Sensors multi-view classification feature fusion industrial inspection open-set classification |
| title | Object-Specific Multiview Classification Through View-Compatible Feature Fusion |
| title_full | Object-Specific Multiview Classification Through View-Compatible Feature Fusion |
| title_fullStr | Object-Specific Multiview Classification Through View-Compatible Feature Fusion |
| title_full_unstemmed | Object-Specific Multiview Classification Through View-Compatible Feature Fusion |
| title_short | Object-Specific Multiview Classification Through View-Compatible Feature Fusion |
| title_sort | object specific multiview classification through view compatible feature fusion |
| topic | multi-view classification feature fusion industrial inspection open-set classification |
| url | https://www.mdpi.com/1424-8220/25/13/4127 |
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