{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:4DEO6KCDEGN3KHUVK6DYI3WQYQ","short_pith_number":"pith:4DEO6KCD","schema_version":"1.0","canonical_sha256":"e0c8ef2843219bb51e955787846ed0c42847117091517cdc62cffc751d05db00","source":{"kind":"arxiv","id":"1709.09304","version":1},"attestation_state":"computed","paper":{"title":"Effective Image Retrieval via Multilinear Multi-index Fusion","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Qi Tian, Wensheng Zhang, Yuan Xie, Zhizhong Zhang","submitted_at":"2017-09-27T01:59:17Z","abstract_excerpt":"Multi-index fusion has demonstrated impressive performances in retrieval task by integrating different visual representations in a unified framework. However, previous works mainly consider propagating similarities via neighbor structure, ignoring the high order information among different visual representations. In this paper, we propose a new multi-index fusion scheme for image retrieval. By formulating this procedure as a multilinear based optimization problem, the complementary information hidden in different indexes can be explored more thoroughly. Specially, we first build our multiple i"},"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":"1709.09304","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-09-27T01:59:17Z","cross_cats_sorted":[],"title_canon_sha256":"55546ba13f4d965f9da1e6baa43abe84893296fae5fe05775337a65b637b6a6d","abstract_canon_sha256":"1d7d25ec8b690fcdb7f456c1794e9e3635c0f63b0f9fa956059b049b1d3222fb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:34:12.243696Z","signature_b64":"yNLWwmE0VSpe7J324M2eAJfI/SMH5v+U4CywniqLYjNPMAaG96E+He9dw6b6X5sDiSVKzltICy40W6phDzi3CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e0c8ef2843219bb51e955787846ed0c42847117091517cdc62cffc751d05db00","last_reissued_at":"2026-05-18T00:34:12.242957Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:34:12.242957Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Effective Image Retrieval via Multilinear Multi-index Fusion","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Qi Tian, Wensheng Zhang, Yuan Xie, Zhizhong Zhang","submitted_at":"2017-09-27T01:59:17Z","abstract_excerpt":"Multi-index fusion has demonstrated impressive performances in retrieval task by integrating different visual representations in a unified framework. However, previous works mainly consider propagating similarities via neighbor structure, ignoring the high order information among different visual representations. In this paper, we propose a new multi-index fusion scheme for image retrieval. By formulating this procedure as a multilinear based optimization problem, the complementary information hidden in different indexes can be explored more thoroughly. Specially, we first build our multiple i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.09304","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":"1709.09304","created_at":"2026-05-18T00:34:12.243064+00:00"},{"alias_kind":"arxiv_version","alias_value":"1709.09304v1","created_at":"2026-05-18T00:34:12.243064+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.09304","created_at":"2026-05-18T00:34:12.243064+00:00"},{"alias_kind":"pith_short_12","alias_value":"4DEO6KCDEGN3","created_at":"2026-05-18T12:30:58.224056+00:00"},{"alias_kind":"pith_short_16","alias_value":"4DEO6KCDEGN3KHUV","created_at":"2026-05-18T12:30:58.224056+00:00"},{"alias_kind":"pith_short_8","alias_value":"4DEO6KCD","created_at":"2026-05-18T12:30:58.224056+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/4DEO6KCDEGN3KHUVK6DYI3WQYQ","json":"https://pith.science/pith/4DEO6KCDEGN3KHUVK6DYI3WQYQ.json","graph_json":"https://pith.science/api/pith-number/4DEO6KCDEGN3KHUVK6DYI3WQYQ/graph.json","events_json":"https://pith.science/api/pith-number/4DEO6KCDEGN3KHUVK6DYI3WQYQ/events.json","paper":"https://pith.science/paper/4DEO6KCD"},"agent_actions":{"view_html":"https://pith.science/pith/4DEO6KCDEGN3KHUVK6DYI3WQYQ","download_json":"https://pith.science/pith/4DEO6KCDEGN3KHUVK6DYI3WQYQ.json","view_paper":"https://pith.science/paper/4DEO6KCD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1709.09304&json=true","fetch_graph":"https://pith.science/api/pith-number/4DEO6KCDEGN3KHUVK6DYI3WQYQ/graph.json","fetch_events":"https://pith.science/api/pith-number/4DEO6KCDEGN3KHUVK6DYI3WQYQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4DEO6KCDEGN3KHUVK6DYI3WQYQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4DEO6KCDEGN3KHUVK6DYI3WQYQ/action/storage_attestation","attest_author":"https://pith.science/pith/4DEO6KCDEGN3KHUVK6DYI3WQYQ/action/author_attestation","sign_citation":"https://pith.science/pith/4DEO6KCDEGN3KHUVK6DYI3WQYQ/action/citation_signature","submit_replication":"https://pith.science/pith/4DEO6KCDEGN3KHUVK6DYI3WQYQ/action/replication_record"}},"created_at":"2026-05-18T00:34:12.243064+00:00","updated_at":"2026-05-18T00:34:12.243064+00:00"}