{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:KLXJFALLVNBGXYCNMDVLIUP7DS","short_pith_number":"pith:KLXJFALL","canonical_record":{"source":{"id":"1407.5245","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2014-07-20T04:42:50Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"6978625bdf7c993026d437688876e0b7df7615c44a23ab82affc4be6da0cc55d","abstract_canon_sha256":"fb2747f8476f1417882f98e19162fc42561f31f8d806a716a4826a7402e3f909"},"schema_version":"1.0"},"canonical_sha256":"52ee92816bab426be04d60eab451ff1c9612c934e6d42945d6f1d27c8e5a0552","source":{"kind":"arxiv","id":"1407.5245","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1407.5245","created_at":"2026-05-18T01:22:43Z"},{"alias_kind":"arxiv_version","alias_value":"1407.5245v2","created_at":"2026-05-18T01:22:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1407.5245","created_at":"2026-05-18T01:22:43Z"},{"alias_kind":"pith_short_12","alias_value":"KLXJFALLVNBG","created_at":"2026-05-18T12:28:35Z"},{"alias_kind":"pith_short_16","alias_value":"KLXJFALLVNBGXYCN","created_at":"2026-05-18T12:28:35Z"},{"alias_kind":"pith_short_8","alias_value":"KLXJFALL","created_at":"2026-05-18T12:28:35Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:KLXJFALLVNBGXYCNMDVLIUP7DS","target":"record","payload":{"canonical_record":{"source":{"id":"1407.5245","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2014-07-20T04:42:50Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"6978625bdf7c993026d437688876e0b7df7615c44a23ab82affc4be6da0cc55d","abstract_canon_sha256":"fb2747f8476f1417882f98e19162fc42561f31f8d806a716a4826a7402e3f909"},"schema_version":"1.0"},"canonical_sha256":"52ee92816bab426be04d60eab451ff1c9612c934e6d42945d6f1d27c8e5a0552","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:22:43.497895Z","signature_b64":"3wt3Lh8FVRl9UXez+F7Uc37NdPGG9392lwa9joYL0uZHqMHtP05PXQg3p6ByTEYuPd6lC5uZSPJhGigr2CdxCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"52ee92816bab426be04d60eab451ff1c9612c934e6d42945d6f1d27c8e5a0552","last_reissued_at":"2026-05-18T01:22:43.497393Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:22:43.497393Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1407.5245","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:22:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"soMjpPBBWFDrMB5osgw+IgCo3xbgVg0XQQrc9ycAJpepHzUnOIIw0sigCoftb4y5KcmgpFjhazn2smrcXV2JAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T21:46:55.252171Z"},"content_sha256":"3468e695ff648b2184f4380c9344aa46c8953c6737963a28ed3296e528a383a8","schema_version":"1.0","event_id":"sha256:3468e695ff648b2184f4380c9344aa46c8953c6737963a28ed3296e528a383a8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:KLXJFALLVNBGXYCNMDVLIUP7DS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Feature and Region Selection for Visual Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Fernando De la Torre, Ji Zhao, LianTao Wang, Ricardo Cabral","submitted_at":"2014-07-20T04:42:50Z","abstract_excerpt":"Visual learning problems such as object classification and action recognition are typically approached using extensions of the popular bag-of-words (BoW) model. Despite its great success, it is unclear what visual features the BoW model is learning: Which regions in the image or video are used to discriminate among classes? Which are the most discriminative visual words? Answering these questions is fundamental for understanding existing BoW models and inspiring better models for visual recognition.\n  To answer these questions, this paper presents a method for feature selection and region sele"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1407.5245","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:22:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DxXkCxY9TSnByiE++LrDXoJ4QLQrPyWLHozmnlJWYjueGlg4M/cvD6IJeyNfnyMVoOXBocWXs/ADIhhEL5YEAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T21:46:55.252858Z"},"content_sha256":"efd759f02d73cd740a988cd31acf8b0b1e1d8bfbd71e968f9fd8643501a64807","schema_version":"1.0","event_id":"sha256:efd759f02d73cd740a988cd31acf8b0b1e1d8bfbd71e968f9fd8643501a64807"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KLXJFALLVNBGXYCNMDVLIUP7DS/bundle.json","state_url":"https://pith.science/pith/KLXJFALLVNBGXYCNMDVLIUP7DS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KLXJFALLVNBGXYCNMDVLIUP7DS/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":"2026-05-25T21:46:55Z","links":{"resolver":"https://pith.science/pith/KLXJFALLVNBGXYCNMDVLIUP7DS","bundle":"https://pith.science/pith/KLXJFALLVNBGXYCNMDVLIUP7DS/bundle.json","state":"https://pith.science/pith/KLXJFALLVNBGXYCNMDVLIUP7DS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KLXJFALLVNBGXYCNMDVLIUP7DS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:KLXJFALLVNBGXYCNMDVLIUP7DS","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":"fb2747f8476f1417882f98e19162fc42561f31f8d806a716a4826a7402e3f909","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2014-07-20T04:42:50Z","title_canon_sha256":"6978625bdf7c993026d437688876e0b7df7615c44a23ab82affc4be6da0cc55d"},"schema_version":"1.0","source":{"id":"1407.5245","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1407.5245","created_at":"2026-05-18T01:22:43Z"},{"alias_kind":"arxiv_version","alias_value":"1407.5245v2","created_at":"2026-05-18T01:22:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1407.5245","created_at":"2026-05-18T01:22:43Z"},{"alias_kind":"pith_short_12","alias_value":"KLXJFALLVNBG","created_at":"2026-05-18T12:28:35Z"},{"alias_kind":"pith_short_16","alias_value":"KLXJFALLVNBGXYCN","created_at":"2026-05-18T12:28:35Z"},{"alias_kind":"pith_short_8","alias_value":"KLXJFALL","created_at":"2026-05-18T12:28:35Z"}],"graph_snapshots":[{"event_id":"sha256:efd759f02d73cd740a988cd31acf8b0b1e1d8bfbd71e968f9fd8643501a64807","target":"graph","created_at":"2026-05-18T01:22:43Z","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":"Visual learning problems such as object classification and action recognition are typically approached using extensions of the popular bag-of-words (BoW) model. Despite its great success, it is unclear what visual features the BoW model is learning: Which regions in the image or video are used to discriminate among classes? Which are the most discriminative visual words? Answering these questions is fundamental for understanding existing BoW models and inspiring better models for visual recognition.\n  To answer these questions, this paper presents a method for feature selection and region sele","authors_text":"Fernando De la Torre, Ji Zhao, LianTao Wang, Ricardo Cabral","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2014-07-20T04:42:50Z","title":"Feature and Region Selection for Visual Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1407.5245","kind":"arxiv","version":2},"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":"sha256:3468e695ff648b2184f4380c9344aa46c8953c6737963a28ed3296e528a383a8","target":"record","created_at":"2026-05-18T01:22:43Z","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":"fb2747f8476f1417882f98e19162fc42561f31f8d806a716a4826a7402e3f909","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2014-07-20T04:42:50Z","title_canon_sha256":"6978625bdf7c993026d437688876e0b7df7615c44a23ab82affc4be6da0cc55d"},"schema_version":"1.0","source":{"id":"1407.5245","kind":"arxiv","version":2}},"canonical_sha256":"52ee92816bab426be04d60eab451ff1c9612c934e6d42945d6f1d27c8e5a0552","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"52ee92816bab426be04d60eab451ff1c9612c934e6d42945d6f1d27c8e5a0552","first_computed_at":"2026-05-18T01:22:43.497393Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:22:43.497393Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"3wt3Lh8FVRl9UXez+F7Uc37NdPGG9392lwa9joYL0uZHqMHtP05PXQg3p6ByTEYuPd6lC5uZSPJhGigr2CdxCg==","signature_status":"signed_v1","signed_at":"2026-05-18T01:22:43.497895Z","signed_message":"canonical_sha256_bytes"},"source_id":"1407.5245","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3468e695ff648b2184f4380c9344aa46c8953c6737963a28ed3296e528a383a8","sha256:efd759f02d73cd740a988cd31acf8b0b1e1d8bfbd71e968f9fd8643501a64807"],"state_sha256":"974b0c08d857fa7b5af7b86ae0ac740d15b712dd0e78d3c16b82da5f79b53120"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7gFF5wiPsWeS+ZHvmSGN99laoe7o5w2lDjc/iAtZ5xglyT9LWYuULTqoQPzhVqNdt3mEWYKKDXoGC12viSRdDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T21:46:55.256528Z","bundle_sha256":"8553127098237690222d2517cca0460bdbc88ea479c1c841f080b85ab4ceef83"}}