{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-graph-theoretic-approach-for-object-shape","title":"A Graph Theoretic Approach for Object Shape Representation in Compositional Hierarchies Using a Hybrid Generative-Descriptive Model","arxiv_id":"1501.05192","date":"2015-01-21","proceeding":null,"authors":["Umit Rusen Aktas","Mete Ozay","Ales Leonardis","Jeremy L. Wyatt"],"abstract":"A graph theoretic approach is proposed for object shape representation in a\nhierarchical compositional architecture called Compositional Hierarchy of Parts\n(CHOP). In the proposed approach, vocabulary learning is performed using a\nhybrid generative-descriptive model. First, statistical relationships between\nparts are learned using a Minimum Conditional Entropy Clustering algorithm.\nThen, selection of descriptive parts is defined as a frequent subgraph\ndiscovery problem, and solved using a Minimum Description Length (MDL)\nprinciple. Finally, part compositions are constructed by compressing the\ninternal data representation with discovered substructures. Shape\nrepresentation and computational complexity properties of the proposed approach\nand algorithms are examined using six benchmark two-dimensional shape image\ndatasets. Experiments show that CHOP can employ part shareability and indexing\nmechanisms for fast inference of part compositions using learned shape\nvocabularies. Additionally, CHOP provides better shape retrieval performance\nthan the state-of-the-art shape retrieval methods.","url_abs":"http://arxiv.org/abs/1501.05192v2","url_pdf":"http://arxiv.org/pdf/1501.05192v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-graph-theoretic-approach-for-object-shape","repo_url":"https://github.com/rusen/CHOP","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"descriptive","task_name":"Descriptive"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}