{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:G76Z4PZB7H3HHYH54XKAN26H7K","short_pith_number":"pith:G76Z4PZB","schema_version":"1.0","canonical_sha256":"37fd9e3f21f9f673e0fde5d406ebc7faad42af53c4244b94d0064d3d1fc9eba7","source":{"kind":"arxiv","id":"1808.05075","version":1},"attestation_state":"computed","paper":{"title":"Multi-feature Fusion for Image Retrieval Using Constrained Dominant Sets","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Leulseged Tesfaye Alemu, Marcello Pelillo","submitted_at":"2018-08-15T13:41:22Z","abstract_excerpt":"Aggregating different image features for image retrieval has recently shown its effectiveness. While highly effective, though, the question of how to uplift the impact of the best features for a specific query image persists as an open computer vision problem. In this paper, we propose a computationally efficient approach to fuse several hand-crafted and deep features, based on the probabilistic distribution of a given membership score of a constrained cluster in an unsupervised manner. First, we introduce an incremental nearest neighbor (NN) selection method, whereby we dynamically select k-N"},"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":"1808.05075","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2018-08-15T13:41:22Z","cross_cats_sorted":[],"title_canon_sha256":"d37a8d791f0edc4c472cd1738ce2225b1e2b5815373c37250085d73bc1fca280","abstract_canon_sha256":"460db53b7984b1e1d53679ae3326e868eb92145d986b8af301e7962554e71f7a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:08:01.694836Z","signature_b64":"AkNebOTSPp5LFXR6znZcY5KkuJVA1ykHx+7NRP+E+US58iPthgrwgNuBV/H/9mCs7Da2IJx58frYJrgOyHX9CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"37fd9e3f21f9f673e0fde5d406ebc7faad42af53c4244b94d0064d3d1fc9eba7","last_reissued_at":"2026-05-18T00:08:01.694262Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:08:01.694262Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multi-feature Fusion for Image Retrieval Using Constrained Dominant Sets","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Leulseged Tesfaye Alemu, Marcello Pelillo","submitted_at":"2018-08-15T13:41:22Z","abstract_excerpt":"Aggregating different image features for image retrieval has recently shown its effectiveness. While highly effective, though, the question of how to uplift the impact of the best features for a specific query image persists as an open computer vision problem. In this paper, we propose a computationally efficient approach to fuse several hand-crafted and deep features, based on the probabilistic distribution of a given membership score of a constrained cluster in an unsupervised manner. First, we introduce an incremental nearest neighbor (NN) selection method, whereby we dynamically select k-N"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.05075","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":"1808.05075","created_at":"2026-05-18T00:08:01.694352+00:00"},{"alias_kind":"arxiv_version","alias_value":"1808.05075v1","created_at":"2026-05-18T00:08:01.694352+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.05075","created_at":"2026-05-18T00:08:01.694352+00:00"},{"alias_kind":"pith_short_12","alias_value":"G76Z4PZB7H3H","created_at":"2026-05-18T12:32:25.280505+00:00"},{"alias_kind":"pith_short_16","alias_value":"G76Z4PZB7H3HHYH5","created_at":"2026-05-18T12:32:25.280505+00:00"},{"alias_kind":"pith_short_8","alias_value":"G76Z4PZB","created_at":"2026-05-18T12:32:25.280505+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/G76Z4PZB7H3HHYH54XKAN26H7K","json":"https://pith.science/pith/G76Z4PZB7H3HHYH54XKAN26H7K.json","graph_json":"https://pith.science/api/pith-number/G76Z4PZB7H3HHYH54XKAN26H7K/graph.json","events_json":"https://pith.science/api/pith-number/G76Z4PZB7H3HHYH54XKAN26H7K/events.json","paper":"https://pith.science/paper/G76Z4PZB"},"agent_actions":{"view_html":"https://pith.science/pith/G76Z4PZB7H3HHYH54XKAN26H7K","download_json":"https://pith.science/pith/G76Z4PZB7H3HHYH54XKAN26H7K.json","view_paper":"https://pith.science/paper/G76Z4PZB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1808.05075&json=true","fetch_graph":"https://pith.science/api/pith-number/G76Z4PZB7H3HHYH54XKAN26H7K/graph.json","fetch_events":"https://pith.science/api/pith-number/G76Z4PZB7H3HHYH54XKAN26H7K/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/G76Z4PZB7H3HHYH54XKAN26H7K/action/timestamp_anchor","attest_storage":"https://pith.science/pith/G76Z4PZB7H3HHYH54XKAN26H7K/action/storage_attestation","attest_author":"https://pith.science/pith/G76Z4PZB7H3HHYH54XKAN26H7K/action/author_attestation","sign_citation":"https://pith.science/pith/G76Z4PZB7H3HHYH54XKAN26H7K/action/citation_signature","submit_replication":"https://pith.science/pith/G76Z4PZB7H3HHYH54XKAN26H7K/action/replication_record"}},"created_at":"2026-05-18T00:08:01.694352+00:00","updated_at":"2026-05-18T00:08:01.694352+00:00"}