{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:QP6ROVD7J3D7V3VKX6AZPFT4Z7","short_pith_number":"pith:QP6ROVD7","schema_version":"1.0","canonical_sha256":"83fd17547f4ec7faeeaabf8197967ccfd930d4c9140024449d20a8857640d598","source":{"kind":"arxiv","id":"1312.7085","version":1},"attestation_state":"computed","paper":{"title":"Finding More Relevance: Propagating Similarity on Markov Random Field for Image Retrieval","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Peng Lu, Xiaojie Wang, Xinshan Zhu, Xujun Peng","submitted_at":"2013-12-26T10:55:14Z","abstract_excerpt":"To effectively retrieve objects from large corpus with high accuracy is a challenge task. In this paper, we propose a method that propagates visual feature level similarities on a Markov random field (MRF) to obtain a high level correspondence in image space for image pairs. The proposed correspondence between image pair reflects not only the similarity of low-level visual features but also the relations built through other images in the database and it can be easily integrated into the existing bag-of-visual-words(BoW) based systems to reduce the missing rate. We evaluate our method on the st"},"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":"1312.7085","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2013-12-26T10:55:14Z","cross_cats_sorted":[],"title_canon_sha256":"d0445a99b0c2cb9fbbd30f4c3082d9443471a0e37b9daa9174c506310ccdbbbc","abstract_canon_sha256":"a857d5c394a88247b59eb7e2cb13223b7bded4be9062d37ee95186c292e7e044"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:03:45.938796Z","signature_b64":"A+LulS4NqUy7ox3uPrtYKh6DsTYPTdTb+xkPAssnUidmxseC6TkPSlyNeE9Bmw5CGzCv29uORdV8BOMNBLVZAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"83fd17547f4ec7faeeaabf8197967ccfd930d4c9140024449d20a8857640d598","last_reissued_at":"2026-05-18T03:03:45.938063Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:03:45.938063Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Finding More Relevance: Propagating Similarity on Markov Random Field for Image Retrieval","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Peng Lu, Xiaojie Wang, Xinshan Zhu, Xujun Peng","submitted_at":"2013-12-26T10:55:14Z","abstract_excerpt":"To effectively retrieve objects from large corpus with high accuracy is a challenge task. In this paper, we propose a method that propagates visual feature level similarities on a Markov random field (MRF) to obtain a high level correspondence in image space for image pairs. The proposed correspondence between image pair reflects not only the similarity of low-level visual features but also the relations built through other images in the database and it can be easily integrated into the existing bag-of-visual-words(BoW) based systems to reduce the missing rate. We evaluate our method on the st"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1312.7085","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":""},"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":"1312.7085","created_at":"2026-05-18T03:03:45.938192+00:00"},{"alias_kind":"arxiv_version","alias_value":"1312.7085v1","created_at":"2026-05-18T03:03:45.938192+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1312.7085","created_at":"2026-05-18T03:03:45.938192+00:00"},{"alias_kind":"pith_short_12","alias_value":"QP6ROVD7J3D7","created_at":"2026-05-18T12:27:57.521954+00:00"},{"alias_kind":"pith_short_16","alias_value":"QP6ROVD7J3D7V3VK","created_at":"2026-05-18T12:27:57.521954+00:00"},{"alias_kind":"pith_short_8","alias_value":"QP6ROVD7","created_at":"2026-05-18T12:27:57.521954+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/QP6ROVD7J3D7V3VKX6AZPFT4Z7","json":"https://pith.science/pith/QP6ROVD7J3D7V3VKX6AZPFT4Z7.json","graph_json":"https://pith.science/api/pith-number/QP6ROVD7J3D7V3VKX6AZPFT4Z7/graph.json","events_json":"https://pith.science/api/pith-number/QP6ROVD7J3D7V3VKX6AZPFT4Z7/events.json","paper":"https://pith.science/paper/QP6ROVD7"},"agent_actions":{"view_html":"https://pith.science/pith/QP6ROVD7J3D7V3VKX6AZPFT4Z7","download_json":"https://pith.science/pith/QP6ROVD7J3D7V3VKX6AZPFT4Z7.json","view_paper":"https://pith.science/paper/QP6ROVD7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1312.7085&json=true","fetch_graph":"https://pith.science/api/pith-number/QP6ROVD7J3D7V3VKX6AZPFT4Z7/graph.json","fetch_events":"https://pith.science/api/pith-number/QP6ROVD7J3D7V3VKX6AZPFT4Z7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QP6ROVD7J3D7V3VKX6AZPFT4Z7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QP6ROVD7J3D7V3VKX6AZPFT4Z7/action/storage_attestation","attest_author":"https://pith.science/pith/QP6ROVD7J3D7V3VKX6AZPFT4Z7/action/author_attestation","sign_citation":"https://pith.science/pith/QP6ROVD7J3D7V3VKX6AZPFT4Z7/action/citation_signature","submit_replication":"https://pith.science/pith/QP6ROVD7J3D7V3VKX6AZPFT4Z7/action/replication_record"}},"created_at":"2026-05-18T03:03:45.938192+00:00","updated_at":"2026-05-18T03:03:45.938192+00:00"}