{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:CFLE4NDAY7YZZ5ABVLCXDDLM6N","short_pith_number":"pith:CFLE4NDA","schema_version":"1.0","canonical_sha256":"11564e3460c7f19cf401aac5718d6cf36f05114e20c123dc958372a68ad05e8b","source":{"kind":"arxiv","id":"2109.02231","version":1},"attestation_state":"computed","paper":{"title":"Image recognition via Vietoris-Rips complex","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jumpei Nagase, Ryotaro Sakamoto, Shiro Takagi, Yasuhiko Asao","submitted_at":"2021-09-06T03:51:10Z","abstract_excerpt":"Extracting informative features from images has been of capital importance in computer vision. In this paper, we propose a way to extract such features from images by a method based on algebraic topology. To that end, we construct a weighted graph from an image, which extracts local information of an image. By considering this weighted graph as a pseudo-metric space, we construct a Vietoris-Rips complex with a parameter $\\varepsilon$ by a well-known process of algebraic topology. We can extract information of complexity of the image and can detect a sub-image with a relatively high concentrati"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2109.02231","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-09-06T03:51:10Z","cross_cats_sorted":[],"title_canon_sha256":"4e665de343af4034eb610b15cbfb1c468e6e4ebe5151026f4be8620a3994dd72","abstract_canon_sha256":"b6065f8e580fc97af8c280a714189a5e2c76cbcc9d4affb9be8da72d6c0cce92"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:11:40.102913Z","signature_b64":"/khyqp7Xvot4EYMBOC9TKODppHWvxWsfZMbnivCcOmSwrrgoUpEgBqnMGZfXAWYT1dzyU6Hf0h6qcZ9ArU/LBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"11564e3460c7f19cf401aac5718d6cf36f05114e20c123dc958372a68ad05e8b","last_reissued_at":"2026-07-05T03:11:40.102581Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:11:40.102581Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Image recognition via Vietoris-Rips complex","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jumpei Nagase, Ryotaro Sakamoto, Shiro Takagi, Yasuhiko Asao","submitted_at":"2021-09-06T03:51:10Z","abstract_excerpt":"Extracting informative features from images has been of capital importance in computer vision. In this paper, we propose a way to extract such features from images by a method based on algebraic topology. To that end, we construct a weighted graph from an image, which extracts local information of an image. By considering this weighted graph as a pseudo-metric space, we construct a Vietoris-Rips complex with a parameter $\\varepsilon$ by a well-known process of algebraic topology. We can extract information of complexity of the image and can detect a sub-image with a relatively high concentrati"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.02231","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2109.02231/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2109.02231","created_at":"2026-07-05T03:11:40.102637+00:00"},{"alias_kind":"arxiv_version","alias_value":"2109.02231v1","created_at":"2026-07-05T03:11:40.102637+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.02231","created_at":"2026-07-05T03:11:40.102637+00:00"},{"alias_kind":"pith_short_12","alias_value":"CFLE4NDAY7YZ","created_at":"2026-07-05T03:11:40.102637+00:00"},{"alias_kind":"pith_short_16","alias_value":"CFLE4NDAY7YZZ5AB","created_at":"2026-07-05T03:11:40.102637+00:00"},{"alias_kind":"pith_short_8","alias_value":"CFLE4NDA","created_at":"2026-07-05T03:11:40.102637+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CFLE4NDAY7YZZ5ABVLCXDDLM6N","json":"https://pith.science/pith/CFLE4NDAY7YZZ5ABVLCXDDLM6N.json","graph_json":"https://pith.science/api/pith-number/CFLE4NDAY7YZZ5ABVLCXDDLM6N/graph.json","events_json":"https://pith.science/api/pith-number/CFLE4NDAY7YZZ5ABVLCXDDLM6N/events.json","paper":"https://pith.science/paper/CFLE4NDA"},"agent_actions":{"view_html":"https://pith.science/pith/CFLE4NDAY7YZZ5ABVLCXDDLM6N","download_json":"https://pith.science/pith/CFLE4NDAY7YZZ5ABVLCXDDLM6N.json","view_paper":"https://pith.science/paper/CFLE4NDA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2109.02231&json=true","fetch_graph":"https://pith.science/api/pith-number/CFLE4NDAY7YZZ5ABVLCXDDLM6N/graph.json","fetch_events":"https://pith.science/api/pith-number/CFLE4NDAY7YZZ5ABVLCXDDLM6N/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CFLE4NDAY7YZZ5ABVLCXDDLM6N/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CFLE4NDAY7YZZ5ABVLCXDDLM6N/action/storage_attestation","attest_author":"https://pith.science/pith/CFLE4NDAY7YZZ5ABVLCXDDLM6N/action/author_attestation","sign_citation":"https://pith.science/pith/CFLE4NDAY7YZZ5ABVLCXDDLM6N/action/citation_signature","submit_replication":"https://pith.science/pith/CFLE4NDAY7YZZ5ABVLCXDDLM6N/action/replication_record"}},"created_at":"2026-07-05T03:11:40.102637+00:00","updated_at":"2026-07-05T03:11:40.102637+00:00"}