{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:MQYFX2THK2E7QBIRUSZFV6P6UC","short_pith_number":"pith:MQYFX2TH","schema_version":"1.0","canonical_sha256":"64305bea675689f80511a4b25af9fea08b61f4eb169076081a2c4345d8d4eb76","source":{"kind":"arxiv","id":"1402.6383","version":1},"attestation_state":"computed","paper":{"title":"Large-margin Learning of Compact Binary Image Encodings","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Anton van den Hengel, Chunhua Shen, Sakrapee Paisitkriangkrai","submitted_at":"2014-02-26T00:22:50Z","abstract_excerpt":"The use of high-dimensional features has become a normal practice in many computer vision applications. The large dimension of these features is a limiting factor upon the number of data points which may be effectively stored and processed, however. We address this problem by developing a novel approach to learning a compact binary encoding, which exploits both pair-wise proximity and class-label information on training data set. Exploiting this extra information allows the development of encodings which, although compact, outperform the original high-dimensional features in terms of final cla"},"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":"1402.6383","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2014-02-26T00:22:50Z","cross_cats_sorted":[],"title_canon_sha256":"7d5a14ace097aec353808eb94e6d7127c4a61cbb6b1310def7dc370c15a21391","abstract_canon_sha256":"b88b83e67a6aa572818c668f45a921dfd2866f347e5b628bb0b401c1eebf43ca"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:44:27.108883Z","signature_b64":"YMCeSAKXYN2gZ5ptPLUgZWkTTm+XS+UHt8XgiMWhks61tnh+1Tc1TgmxdJm7GVOSq8LYxNcfsCKL98pxIT+GBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"64305bea675689f80511a4b25af9fea08b61f4eb169076081a2c4345d8d4eb76","last_reissued_at":"2026-05-18T01:44:27.108518Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:44:27.108518Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Large-margin Learning of Compact Binary Image Encodings","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Anton van den Hengel, Chunhua Shen, Sakrapee Paisitkriangkrai","submitted_at":"2014-02-26T00:22:50Z","abstract_excerpt":"The use of high-dimensional features has become a normal practice in many computer vision applications. The large dimension of these features is a limiting factor upon the number of data points which may be effectively stored and processed, however. We address this problem by developing a novel approach to learning a compact binary encoding, which exploits both pair-wise proximity and class-label information on training data set. Exploiting this extra information allows the development of encodings which, although compact, outperform the original high-dimensional features in terms of final cla"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1402.6383","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":"1402.6383","created_at":"2026-05-18T01:44:27.108573+00:00"},{"alias_kind":"arxiv_version","alias_value":"1402.6383v1","created_at":"2026-05-18T01:44:27.108573+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1402.6383","created_at":"2026-05-18T01:44:27.108573+00:00"},{"alias_kind":"pith_short_12","alias_value":"MQYFX2THK2E7","created_at":"2026-05-18T12:28:38.356838+00:00"},{"alias_kind":"pith_short_16","alias_value":"MQYFX2THK2E7QBIR","created_at":"2026-05-18T12:28:38.356838+00:00"},{"alias_kind":"pith_short_8","alias_value":"MQYFX2TH","created_at":"2026-05-18T12:28:38.356838+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/MQYFX2THK2E7QBIRUSZFV6P6UC","json":"https://pith.science/pith/MQYFX2THK2E7QBIRUSZFV6P6UC.json","graph_json":"https://pith.science/api/pith-number/MQYFX2THK2E7QBIRUSZFV6P6UC/graph.json","events_json":"https://pith.science/api/pith-number/MQYFX2THK2E7QBIRUSZFV6P6UC/events.json","paper":"https://pith.science/paper/MQYFX2TH"},"agent_actions":{"view_html":"https://pith.science/pith/MQYFX2THK2E7QBIRUSZFV6P6UC","download_json":"https://pith.science/pith/MQYFX2THK2E7QBIRUSZFV6P6UC.json","view_paper":"https://pith.science/paper/MQYFX2TH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1402.6383&json=true","fetch_graph":"https://pith.science/api/pith-number/MQYFX2THK2E7QBIRUSZFV6P6UC/graph.json","fetch_events":"https://pith.science/api/pith-number/MQYFX2THK2E7QBIRUSZFV6P6UC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MQYFX2THK2E7QBIRUSZFV6P6UC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MQYFX2THK2E7QBIRUSZFV6P6UC/action/storage_attestation","attest_author":"https://pith.science/pith/MQYFX2THK2E7QBIRUSZFV6P6UC/action/author_attestation","sign_citation":"https://pith.science/pith/MQYFX2THK2E7QBIRUSZFV6P6UC/action/citation_signature","submit_replication":"https://pith.science/pith/MQYFX2THK2E7QBIRUSZFV6P6UC/action/replication_record"}},"created_at":"2026-05-18T01:44:27.108573+00:00","updated_at":"2026-05-18T01:44:27.108573+00:00"}