{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:J5L5FGQUBJ3UMWOJEMQESBUCCS","short_pith_number":"pith:J5L5FGQU","schema_version":"1.0","canonical_sha256":"4f57d29a140a774659c9232049068214819c31f1dce1684a58fda9f6fe0e2165","source":{"kind":"arxiv","id":"1406.1134","version":2},"attestation_state":"computed","paper":{"title":"Local Decorrelation For Improved Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Joon Hee Han, Piotr Doll\\'ar, Woonhyun Nam","submitted_at":"2014-06-04T18:20:38Z","abstract_excerpt":"Even with the advent of more sophisticated, data-hungry methods, boosted decision trees remain extraordinarily successful for fast rigid object detection, achieving top accuracy on numerous datasets. While effective, most boosted detectors use decision trees with orthogonal (single feature) splits, and the topology of the resulting decision boundary may not be well matched to the natural topology of the data. Given highly correlated data, decision trees with oblique (multiple feature) splits can be effective. Use of oblique splits, however, comes at considerable computational expense. Inspired"},"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":"1406.1134","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2014-06-04T18:20:38Z","cross_cats_sorted":[],"title_canon_sha256":"7ce2ed2c07631340359b442d94c364b0ac664a5d5d6a790e025fae0754f9c5bb","abstract_canon_sha256":"4412ae707b92e4daa223d1f4dde9ad35973f280a631f29de1deb385449c7e680"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:38:44.036489Z","signature_b64":"O7whjF35s9QNtLc1EyFyK7+se2SrLzRdCiBYjFfUXqQe+4AS/dHu6CYj+GmVq7LW6lGDfinx/yJqwa0dTVIzAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4f57d29a140a774659c9232049068214819c31f1dce1684a58fda9f6fe0e2165","last_reissued_at":"2026-05-18T02:38:44.036038Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:38:44.036038Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Local Decorrelation For Improved Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Joon Hee Han, Piotr Doll\\'ar, Woonhyun Nam","submitted_at":"2014-06-04T18:20:38Z","abstract_excerpt":"Even with the advent of more sophisticated, data-hungry methods, boosted decision trees remain extraordinarily successful for fast rigid object detection, achieving top accuracy on numerous datasets. While effective, most boosted detectors use decision trees with orthogonal (single feature) splits, and the topology of the resulting decision boundary may not be well matched to the natural topology of the data. Given highly correlated data, decision trees with oblique (multiple feature) splits can be effective. Use of oblique splits, however, comes at considerable computational expense. Inspired"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1406.1134","kind":"arxiv","version":2},"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":"1406.1134","created_at":"2026-05-18T02:38:44.036105+00:00"},{"alias_kind":"arxiv_version","alias_value":"1406.1134v2","created_at":"2026-05-18T02:38:44.036105+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1406.1134","created_at":"2026-05-18T02:38:44.036105+00:00"},{"alias_kind":"pith_short_12","alias_value":"J5L5FGQUBJ3U","created_at":"2026-05-18T12:28:33.132498+00:00"},{"alias_kind":"pith_short_16","alias_value":"J5L5FGQUBJ3UMWOJ","created_at":"2026-05-18T12:28:33.132498+00:00"},{"alias_kind":"pith_short_8","alias_value":"J5L5FGQU","created_at":"2026-05-18T12:28:33.132498+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2002.09053","citing_title":"Adapted Center and Scale Prediction: More Stable and More Accurate","ref_index":27,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/J5L5FGQUBJ3UMWOJEMQESBUCCS","json":"https://pith.science/pith/J5L5FGQUBJ3UMWOJEMQESBUCCS.json","graph_json":"https://pith.science/api/pith-number/J5L5FGQUBJ3UMWOJEMQESBUCCS/graph.json","events_json":"https://pith.science/api/pith-number/J5L5FGQUBJ3UMWOJEMQESBUCCS/events.json","paper":"https://pith.science/paper/J5L5FGQU"},"agent_actions":{"view_html":"https://pith.science/pith/J5L5FGQUBJ3UMWOJEMQESBUCCS","download_json":"https://pith.science/pith/J5L5FGQUBJ3UMWOJEMQESBUCCS.json","view_paper":"https://pith.science/paper/J5L5FGQU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1406.1134&json=true","fetch_graph":"https://pith.science/api/pith-number/J5L5FGQUBJ3UMWOJEMQESBUCCS/graph.json","fetch_events":"https://pith.science/api/pith-number/J5L5FGQUBJ3UMWOJEMQESBUCCS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/J5L5FGQUBJ3UMWOJEMQESBUCCS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/J5L5FGQUBJ3UMWOJEMQESBUCCS/action/storage_attestation","attest_author":"https://pith.science/pith/J5L5FGQUBJ3UMWOJEMQESBUCCS/action/author_attestation","sign_citation":"https://pith.science/pith/J5L5FGQUBJ3UMWOJEMQESBUCCS/action/citation_signature","submit_replication":"https://pith.science/pith/J5L5FGQUBJ3UMWOJEMQESBUCCS/action/replication_record"}},"created_at":"2026-05-18T02:38:44.036105+00:00","updated_at":"2026-05-18T02:38:44.036105+00:00"}