TOSD: A Hierarchical Object-Centric Descriptor Integrating Shape, Color, and Topology
This paper introduces a hierarchical object-centric descriptor framework called TOSD (Triplet Object-Centric Semantic Descriptor). The goal of this method is to overcome the limitations of existing pixel-based and global feature embedding approaches. To this end, the framework adopts a hierarchical...
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MDPI AG
2025-07-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/15/4614 |
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| author | Jun-Hyeon Choi Jeong-Won Pyo Ye-Chan An Tae-Yong Kuc |
| author_facet | Jun-Hyeon Choi Jeong-Won Pyo Ye-Chan An Tae-Yong Kuc |
| author_sort | Jun-Hyeon Choi |
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| description | This paper introduces a hierarchical object-centric descriptor framework called TOSD (Triplet Object-Centric Semantic Descriptor). The goal of this method is to overcome the limitations of existing pixel-based and global feature embedding approaches. To this end, the framework adopts a hierarchical representation that is explicitly designed for multi-level reasoning. TOSD combines shape, color, and topological information without depending on predefined class labels. The shape descriptor captures the geometric configuration of each object. The color descriptor focuses on internal appearance by extracting normalized color features. The topology descriptor models the spatial and semantic relationships between objects in a scene. These components are integrated at both object and scene levels to produce compact and consistent embeddings. The resulting representation covers three levels of abstraction: low-level pixel details, mid-level object features, and high-level semantic structure. This hierarchical organization makes it possible to represent both local cues and global context in a unified form. We evaluate the proposed method on multiple vision tasks. The results show that TOSD performs competitively compared to baseline methods, while maintaining robustness in challenging cases such as occlusion and viewpoint changes. The framework is applicable to visual odometry, SLAM, object tracking, global localization, scene clustering, and image retrieval. In addition, this work extends our previous research on the <i>Semantic Modeling Framework</i>, which represents environments using layered structures of places, objects, and their ontological relations. |
| format | Article |
| id | doaj-art-81fe4d8045f44fbcb9b80edcf6090f94 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
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| spelling | doaj-art-81fe4d8045f44fbcb9b80edcf6090f942025-08-20T04:00:49ZengMDPI AGSensors1424-82202025-07-012515461410.3390/s25154614TOSD: A Hierarchical Object-Centric Descriptor Integrating Shape, Color, and TopologyJun-Hyeon Choi0Jeong-Won Pyo1Ye-Chan An2Tae-Yong Kuc3Department of Electrical and Computer Engineering, College of Information and Communication Engineering, Sungkyunkwan University, Suwon 16419, Republic of KoreaR&D Center, DXR Co., Ltd., Seoul 01411, Republic of KoreaDepartment of Electrical and Computer Engineering, College of Information and Communication Engineering, Sungkyunkwan University, Suwon 16419, Republic of KoreaDepartment of Electrical and Computer Engineering, College of Information and Communication Engineering, Sungkyunkwan University, Suwon 16419, Republic of KoreaThis paper introduces a hierarchical object-centric descriptor framework called TOSD (Triplet Object-Centric Semantic Descriptor). The goal of this method is to overcome the limitations of existing pixel-based and global feature embedding approaches. To this end, the framework adopts a hierarchical representation that is explicitly designed for multi-level reasoning. TOSD combines shape, color, and topological information without depending on predefined class labels. The shape descriptor captures the geometric configuration of each object. The color descriptor focuses on internal appearance by extracting normalized color features. The topology descriptor models the spatial and semantic relationships between objects in a scene. These components are integrated at both object and scene levels to produce compact and consistent embeddings. The resulting representation covers three levels of abstraction: low-level pixel details, mid-level object features, and high-level semantic structure. This hierarchical organization makes it possible to represent both local cues and global context in a unified form. We evaluate the proposed method on multiple vision tasks. The results show that TOSD performs competitively compared to baseline methods, while maintaining robustness in challenging cases such as occlusion and viewpoint changes. The framework is applicable to visual odometry, SLAM, object tracking, global localization, scene clustering, and image retrieval. In addition, this work extends our previous research on the <i>Semantic Modeling Framework</i>, which represents environments using layered structures of places, objects, and their ontological relations.https://www.mdpi.com/1424-8220/25/15/4614hierarchical descriptorvisual representationscene understandingobject poolingfeature aggregation |
| spellingShingle | Jun-Hyeon Choi Jeong-Won Pyo Ye-Chan An Tae-Yong Kuc TOSD: A Hierarchical Object-Centric Descriptor Integrating Shape, Color, and Topology Sensors hierarchical descriptor visual representation scene understanding object pooling feature aggregation |
| title | TOSD: A Hierarchical Object-Centric Descriptor Integrating Shape, Color, and Topology |
| title_full | TOSD: A Hierarchical Object-Centric Descriptor Integrating Shape, Color, and Topology |
| title_fullStr | TOSD: A Hierarchical Object-Centric Descriptor Integrating Shape, Color, and Topology |
| title_full_unstemmed | TOSD: A Hierarchical Object-Centric Descriptor Integrating Shape, Color, and Topology |
| title_short | TOSD: A Hierarchical Object-Centric Descriptor Integrating Shape, Color, and Topology |
| title_sort | tosd a hierarchical object centric descriptor integrating shape color and topology |
| topic | hierarchical descriptor visual representation scene understanding object pooling feature aggregation |
| url | https://www.mdpi.com/1424-8220/25/15/4614 |
| work_keys_str_mv | AT junhyeonchoi tosdahierarchicalobjectcentricdescriptorintegratingshapecolorandtopology AT jeongwonpyo tosdahierarchicalobjectcentricdescriptorintegratingshapecolorandtopology AT yechanan tosdahierarchicalobjectcentricdescriptorintegratingshapecolorandtopology AT taeyongkuc tosdahierarchicalobjectcentricdescriptorintegratingshapecolorandtopology |