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Two-dimensional spatial orientation relation recognition between image objects
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AI
DOI：10.1016/j.jestch.2025.102074.png)
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Abstract
En 中文
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.
Keywords:
Target spatial orientation vector field
Spatial Orientation Relation
Recognition
Journal
E
IF:
5.4
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1.3K
Citations:
6.3K
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