{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:YRS56DTCI44HPLFJDMEVDGZIM6","short_pith_number":"pith:YRS56DTC","schema_version":"1.0","canonical_sha256":"c465df0e62473877aca91b09519b286791bb9ded8225f706f78b8282ecd7970b","source":{"kind":"arxiv","id":"1708.02668","version":1},"attestation_state":"computed","paper":{"title":"A discriminative view of MRF pre-processing algorithms","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Charles Herrmann, Chen Wang, Ramin Zabih","submitted_at":"2017-08-08T22:41:43Z","abstract_excerpt":"While Markov Random Fields (MRFs) are widely used in computer vision, they present a quite challenging inference problem. MRF inference can be accelerated by pre-processing techniques like Dead End Elimination (DEE) or QPBO-based approaches which compute the optimal labeling of a subset of variables. These techniques are guaranteed to never wrongly label a variable but they often leave a large number of variables unlabeled. We address this shortcoming by interpreting pre-processing as a classification problem, which allows us to trade off false positives (i.e., giving a variable an incorrect l"},"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":"1708.02668","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-08T22:41:43Z","cross_cats_sorted":[],"title_canon_sha256":"bc2eab6c8f9a6086dd0453f6dc5fa260820ce24f03c78f7cb05953e31331aa54","abstract_canon_sha256":"4706ed008b8d85e50d22b6fb7c7a1e567ccd1b4245b5193976f5114c54f72370"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:38:19.973586Z","signature_b64":"AqwnJyLcmxw6QRCi0TiQ94dz0VczwSW1GwEGukyG6X2Kw42iB0TaQ6M/KhH0Q+VIT9s++4C8XyDTNA/fQBBbDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c465df0e62473877aca91b09519b286791bb9ded8225f706f78b8282ecd7970b","last_reissued_at":"2026-05-18T00:38:19.972941Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:38:19.972941Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A discriminative view of MRF pre-processing algorithms","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Charles Herrmann, Chen Wang, Ramin Zabih","submitted_at":"2017-08-08T22:41:43Z","abstract_excerpt":"While Markov Random Fields (MRFs) are widely used in computer vision, they present a quite challenging inference problem. MRF inference can be accelerated by pre-processing techniques like Dead End Elimination (DEE) or QPBO-based approaches which compute the optimal labeling of a subset of variables. These techniques are guaranteed to never wrongly label a variable but they often leave a large number of variables unlabeled. We address this shortcoming by interpreting pre-processing as a classification problem, which allows us to trade off false positives (i.e., giving a variable an incorrect l"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.02668","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":"1708.02668","created_at":"2026-05-18T00:38:19.973086+00:00"},{"alias_kind":"arxiv_version","alias_value":"1708.02668v1","created_at":"2026-05-18T00:38:19.973086+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.02668","created_at":"2026-05-18T00:38:19.973086+00:00"},{"alias_kind":"pith_short_12","alias_value":"YRS56DTCI44H","created_at":"2026-05-18T12:31:56.362134+00:00"},{"alias_kind":"pith_short_16","alias_value":"YRS56DTCI44HPLFJ","created_at":"2026-05-18T12:31:56.362134+00:00"},{"alias_kind":"pith_short_8","alias_value":"YRS56DTC","created_at":"2026-05-18T12:31:56.362134+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/YRS56DTCI44HPLFJDMEVDGZIM6","json":"https://pith.science/pith/YRS56DTCI44HPLFJDMEVDGZIM6.json","graph_json":"https://pith.science/api/pith-number/YRS56DTCI44HPLFJDMEVDGZIM6/graph.json","events_json":"https://pith.science/api/pith-number/YRS56DTCI44HPLFJDMEVDGZIM6/events.json","paper":"https://pith.science/paper/YRS56DTC"},"agent_actions":{"view_html":"https://pith.science/pith/YRS56DTCI44HPLFJDMEVDGZIM6","download_json":"https://pith.science/pith/YRS56DTCI44HPLFJDMEVDGZIM6.json","view_paper":"https://pith.science/paper/YRS56DTC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1708.02668&json=true","fetch_graph":"https://pith.science/api/pith-number/YRS56DTCI44HPLFJDMEVDGZIM6/graph.json","fetch_events":"https://pith.science/api/pith-number/YRS56DTCI44HPLFJDMEVDGZIM6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YRS56DTCI44HPLFJDMEVDGZIM6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YRS56DTCI44HPLFJDMEVDGZIM6/action/storage_attestation","attest_author":"https://pith.science/pith/YRS56DTCI44HPLFJDMEVDGZIM6/action/author_attestation","sign_citation":"https://pith.science/pith/YRS56DTCI44HPLFJDMEVDGZIM6/action/citation_signature","submit_replication":"https://pith.science/pith/YRS56DTCI44HPLFJDMEVDGZIM6/action/replication_record"}},"created_at":"2026-05-18T00:38:19.973086+00:00","updated_at":"2026-05-18T00:38:19.973086+00:00"}