{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:U7VFZ57TKPWYHVLFOOMNJW4EMH","short_pith_number":"pith:U7VFZ57T","canonical_record":{"source":{"id":"1806.05946","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-15T13:18:59Z","cross_cats_sorted":[],"title_canon_sha256":"0e99caef42bc2e9a473bd78d2de035410c83d512353a1e896bd6b7ae2e923239","abstract_canon_sha256":"e6953288729b576eec9067272c48db4849f4f419e46767d74f63ec52ee92317c"},"schema_version":"1.0"},"canonical_sha256":"a7ea5cf7f353ed83d5657398d4db8461f6b2df0e7216f8b5a71b65e1631a568d","source":{"kind":"arxiv","id":"1806.05946","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.05946","created_at":"2026-05-18T00:13:08Z"},{"alias_kind":"arxiv_version","alias_value":"1806.05946v1","created_at":"2026-05-18T00:13:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.05946","created_at":"2026-05-18T00:13:08Z"},{"alias_kind":"pith_short_12","alias_value":"U7VFZ57TKPWY","created_at":"2026-05-18T12:32:56Z"},{"alias_kind":"pith_short_16","alias_value":"U7VFZ57TKPWYHVLF","created_at":"2026-05-18T12:32:56Z"},{"alias_kind":"pith_short_8","alias_value":"U7VFZ57T","created_at":"2026-05-18T12:32:56Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:U7VFZ57TKPWYHVLFOOMNJW4EMH","target":"record","payload":{"canonical_record":{"source":{"id":"1806.05946","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-15T13:18:59Z","cross_cats_sorted":[],"title_canon_sha256":"0e99caef42bc2e9a473bd78d2de035410c83d512353a1e896bd6b7ae2e923239","abstract_canon_sha256":"e6953288729b576eec9067272c48db4849f4f419e46767d74f63ec52ee92317c"},"schema_version":"1.0"},"canonical_sha256":"a7ea5cf7f353ed83d5657398d4db8461f6b2df0e7216f8b5a71b65e1631a568d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:13:08.950986Z","signature_b64":"GkNxCLuBiJLe/sLSrfJeVaZYQQpa2TOm03RGOVTD+GD2HOK+m29eJMChxe6Fg9XZBlO8VIouC2Mfl1BviMT1AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a7ea5cf7f353ed83d5657398d4db8461f6b2df0e7216f8b5a71b65e1631a568d","last_reissued_at":"2026-05-18T00:13:08.950274Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:13:08.950274Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1806.05946","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:13:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2buW46OHQylUmA4DlYHxgsKIx5UlcBsAJ2BkqQSD+gkUsB/PTW2GECo/I8An9wtKhVoY//02SghiXp7pjcGVAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T17:40:49.401590Z"},"content_sha256":"f915bc1bd81473582150d9336bf7d4fd0097405153230047b5182c607c27203e","schema_version":"1.0","event_id":"sha256:f915bc1bd81473582150d9336bf7d4fd0097405153230047b5182c607c27203e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:U7VFZ57TKPWYHVLFOOMNJW4EMH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Efficient Nearest Neighbors Search for Large-Scale Landmark Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Andrea Prati, Federico Magliani, Tomaso Fontanini","submitted_at":"2018-06-15T13:18:59Z","abstract_excerpt":"The problem of landmark recognition has achieved excellent results in small-scale datasets. When dealing with large-scale retrieval, issues that were irrelevant with small amount of data, quickly become fundamental for an efficient retrieval phase. In particular, computational time needs to be kept as low as possible, whilst the retrieval accuracy has to be preserved as much as possible. In this paper we propose a novel multi-index hashing method called Bag of Indexes (BoI) for Approximate Nearest Neighbors (ANN) search. It allows to drastically reduce the query time and outperforms the accura"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.05946","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:13:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Fj/JJ8VLhYjfaqvTAkYkqgxTjdYBqWD2pr9/wHYq7Y4V8Nim9njSc9tPUm9Kfbv09JQzC+Y2J8HL0leabAg0Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T17:40:49.402055Z"},"content_sha256":"ecf71887d57fa6b9cacc0108f4ddc348bccfdd5e7aa677bbc0494ca3c7436a53","schema_version":"1.0","event_id":"sha256:ecf71887d57fa6b9cacc0108f4ddc348bccfdd5e7aa677bbc0494ca3c7436a53"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/U7VFZ57TKPWYHVLFOOMNJW4EMH/bundle.json","state_url":"https://pith.science/pith/U7VFZ57TKPWYHVLFOOMNJW4EMH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/U7VFZ57TKPWYHVLFOOMNJW4EMH/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-03T17:40:49Z","links":{"resolver":"https://pith.science/pith/U7VFZ57TKPWYHVLFOOMNJW4EMH","bundle":"https://pith.science/pith/U7VFZ57TKPWYHVLFOOMNJW4EMH/bundle.json","state":"https://pith.science/pith/U7VFZ57TKPWYHVLFOOMNJW4EMH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/U7VFZ57TKPWYHVLFOOMNJW4EMH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:U7VFZ57TKPWYHVLFOOMNJW4EMH","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"e6953288729b576eec9067272c48db4849f4f419e46767d74f63ec52ee92317c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-15T13:18:59Z","title_canon_sha256":"0e99caef42bc2e9a473bd78d2de035410c83d512353a1e896bd6b7ae2e923239"},"schema_version":"1.0","source":{"id":"1806.05946","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.05946","created_at":"2026-05-18T00:13:08Z"},{"alias_kind":"arxiv_version","alias_value":"1806.05946v1","created_at":"2026-05-18T00:13:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.05946","created_at":"2026-05-18T00:13:08Z"},{"alias_kind":"pith_short_12","alias_value":"U7VFZ57TKPWY","created_at":"2026-05-18T12:32:56Z"},{"alias_kind":"pith_short_16","alias_value":"U7VFZ57TKPWYHVLF","created_at":"2026-05-18T12:32:56Z"},{"alias_kind":"pith_short_8","alias_value":"U7VFZ57T","created_at":"2026-05-18T12:32:56Z"}],"graph_snapshots":[{"event_id":"sha256:ecf71887d57fa6b9cacc0108f4ddc348bccfdd5e7aa677bbc0494ca3c7436a53","target":"graph","created_at":"2026-05-18T00:13:08Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"The problem of landmark recognition has achieved excellent results in small-scale datasets. When dealing with large-scale retrieval, issues that were irrelevant with small amount of data, quickly become fundamental for an efficient retrieval phase. In particular, computational time needs to be kept as low as possible, whilst the retrieval accuracy has to be preserved as much as possible. In this paper we propose a novel multi-index hashing method called Bag of Indexes (BoI) for Approximate Nearest Neighbors (ANN) search. It allows to drastically reduce the query time and outperforms the accura","authors_text":"Andrea Prati, Federico Magliani, Tomaso Fontanini","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-15T13:18:59Z","title":"Efficient Nearest Neighbors Search for Large-Scale Landmark Recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.05946","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:f915bc1bd81473582150d9336bf7d4fd0097405153230047b5182c607c27203e","target":"record","created_at":"2026-05-18T00:13:08Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"e6953288729b576eec9067272c48db4849f4f419e46767d74f63ec52ee92317c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-15T13:18:59Z","title_canon_sha256":"0e99caef42bc2e9a473bd78d2de035410c83d512353a1e896bd6b7ae2e923239"},"schema_version":"1.0","source":{"id":"1806.05946","kind":"arxiv","version":1}},"canonical_sha256":"a7ea5cf7f353ed83d5657398d4db8461f6b2df0e7216f8b5a71b65e1631a568d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a7ea5cf7f353ed83d5657398d4db8461f6b2df0e7216f8b5a71b65e1631a568d","first_computed_at":"2026-05-18T00:13:08.950274Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:13:08.950274Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"GkNxCLuBiJLe/sLSrfJeVaZYQQpa2TOm03RGOVTD+GD2HOK+m29eJMChxe6Fg9XZBlO8VIouC2Mfl1BviMT1AA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:13:08.950986Z","signed_message":"canonical_sha256_bytes"},"source_id":"1806.05946","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f915bc1bd81473582150d9336bf7d4fd0097405153230047b5182c607c27203e","sha256:ecf71887d57fa6b9cacc0108f4ddc348bccfdd5e7aa677bbc0494ca3c7436a53"],"state_sha256":"5a5adc4d72f3fcbb30bd600187a18281f28b46aababf5506b996d46f0e5e8a66"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Tjycue3jQq4xFXl9YstLVpmBS+1afZu0cYTgppTuQtfyt61rpYn3kK69mxcnA22cYEkNa+CPxwvE7IJ/e55dCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T17:40:49.407032Z","bundle_sha256":"795ed5b080f683c7c81117193edf60a2ac9a825d9fdbedef4df606472cd2f5c7"}}