Two-dimensional spatial orientation relation recognition between image objects
Recent advances in computer vision have concentrated on comprehension of the semantic features of images, particularly the spatial relations between objects—a fundamental semantic feature of visual scene understanding. This study systematically addresses the recognition problem of two-dimensional sp...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-07-01
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| Series: | Engineering Science and Technology, an International Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215098625001296 |
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| author | Gong Peiyong Zheng Kai Jiang Yi Zhao Huixuan Huai Honghao Guan Ruijie |
| author_facet | Gong Peiyong Zheng Kai Jiang Yi Zhao Huixuan Huai Honghao Guan Ruijie |
| author_sort | Gong Peiyong |
| collection | DOAJ |
| description | Recent advances in computer vision have concentrated on comprehension of the semantic features of images, particularly the spatial relations between objects—a fundamental semantic feature of visual scene understanding. This study systematically addresses the recognition problem of two-dimensional spatial orientation relations and develops the Target Spatial Orientation Vector Field (TSOVF) algorithm, a novel end-to-end framework to explicitly model spatial orientation dependencies. TSOVF algorithm introduces the learnable spatial orientation vector field to effectively encode the spatial orientation relation into a deep convolutional neural network model. The proposed architecture features a dual-branch design: the T-branch identifies object central points and classifies categories via keypoint estimation, while the S-branch constructs a pixel-level spatial orientation vector field. Each vector in this field quantifies the angular orientation between object pairs, with aggregated vector data determining the final spatial relation category. A dedicated fusion module synthesizes features from both branches, generating a structured triple list that documents detected objects, their inter-object spatial orientations, and associated confidence scores. Evaluated on a PASCAL VOC2012-derived dataset, TSOVF algorithm achieves 94.8 % global accuracy and a class-balanced geometric mean (G-mean) of 0.798, demonstrating robust performance across various spatial configurations. For dominant orientation categories, the algorithm attains up to 95.9 % precision and 94.7 % F1-score, establishing it as a foundational benchmark for spatial relation recognition. These results validate TSOVF’s capacity to advance fine-grained visual relationship detection while providing a reproducible framework for future research in spatial-semantic analysis. |
| format | Article |
| id | doaj-art-bdb795518ade4a7ead3886bcc1add38a |
| institution | Kabale University |
| issn | 2215-0986 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Engineering Science and Technology, an International Journal |
| spelling | doaj-art-bdb795518ade4a7ead3886bcc1add38a2025-08-20T03:48:11ZengElsevierEngineering Science and Technology, an International Journal2215-09862025-07-016710207410.1016/j.jestch.2025.102074Two-dimensional spatial orientation relation recognition between image objectsGong Peiyong0Zheng Kai1Jiang Yi2Zhao Huixuan3Huai Honghao4Guan Ruijie5Dalian Maritime University, Marine Electrical Engineering College, Dalian 116026, ChinaDalian Maritime University, Marine Electrical Engineering College, Dalian 116026, China; Corresponding author.Dalian Maritime University, Science and Technology College, Dalian 116026, ChinaDalian Maritime University, Marine Electrical Engineering College, Dalian 116026, ChinaDalian Maritime University, Marine Electrical Engineering College, Dalian 116026, ChinaDalian Maritime University, Marine Electrical Engineering College, Dalian 116026, ChinaRecent advances in computer vision have concentrated on comprehension of the semantic features of images, particularly the spatial relations between objects—a fundamental semantic feature of visual scene understanding. This study systematically addresses the recognition problem of two-dimensional spatial orientation relations and develops the Target Spatial Orientation Vector Field (TSOVF) algorithm, a novel end-to-end framework to explicitly model spatial orientation dependencies. TSOVF algorithm introduces the learnable spatial orientation vector field to effectively encode the spatial orientation relation into a deep convolutional neural network model. The proposed architecture features a dual-branch design: the T-branch identifies object central points and classifies categories via keypoint estimation, while the S-branch constructs a pixel-level spatial orientation vector field. Each vector in this field quantifies the angular orientation between object pairs, with aggregated vector data determining the final spatial relation category. A dedicated fusion module synthesizes features from both branches, generating a structured triple list that documents detected objects, their inter-object spatial orientations, and associated confidence scores. Evaluated on a PASCAL VOC2012-derived dataset, TSOVF algorithm achieves 94.8 % global accuracy and a class-balanced geometric mean (G-mean) of 0.798, demonstrating robust performance across various spatial configurations. For dominant orientation categories, the algorithm attains up to 95.9 % precision and 94.7 % F1-score, establishing it as a foundational benchmark for spatial relation recognition. These results validate TSOVF’s capacity to advance fine-grained visual relationship detection while providing a reproducible framework for future research in spatial-semantic analysis.http://www.sciencedirect.com/science/article/pii/S2215098625001296Target spatial orientation vector fieldSpatial Orientation RelationRecognition |
| spellingShingle | Gong Peiyong Zheng Kai Jiang Yi Zhao Huixuan Huai Honghao Guan Ruijie Two-dimensional spatial orientation relation recognition between image objects Engineering Science and Technology, an International Journal Target spatial orientation vector field Spatial Orientation Relation Recognition |
| title | Two-dimensional spatial orientation relation recognition between image objects |
| title_full | Two-dimensional spatial orientation relation recognition between image objects |
| title_fullStr | Two-dimensional spatial orientation relation recognition between image objects |
| title_full_unstemmed | Two-dimensional spatial orientation relation recognition between image objects |
| title_short | Two-dimensional spatial orientation relation recognition between image objects |
| title_sort | two dimensional spatial orientation relation recognition between image objects |
| topic | Target spatial orientation vector field Spatial Orientation Relation Recognition |
| url | http://www.sciencedirect.com/science/article/pii/S2215098625001296 |
| work_keys_str_mv | AT gongpeiyong twodimensionalspatialorientationrelationrecognitionbetweenimageobjects AT zhengkai twodimensionalspatialorientationrelationrecognitionbetweenimageobjects AT jiangyi twodimensionalspatialorientationrelationrecognitionbetweenimageobjects AT zhaohuixuan twodimensionalspatialorientationrelationrecognitionbetweenimageobjects AT huaihonghao twodimensionalspatialorientationrelationrecognitionbetweenimageobjects AT guanruijie twodimensionalspatialorientationrelationrecognitionbetweenimageobjects |