{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:UVLHN6UGD454Z64UOOAQZYDMEN","short_pith_number":"pith:UVLHN6UG","canonical_record":{"source":{"id":"1712.00575","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-12-02T09:08:05Z","cross_cats_sorted":[],"title_canon_sha256":"b814cbe9e92674c5df9473feee004d7acdcbc310bb944f31742a5034788dfa8c","abstract_canon_sha256":"152e8939708567685d7044201179e8efecffea67b2d7ac58709a6bfeb0324066"},"schema_version":"1.0"},"canonical_sha256":"a55676fa861f3bccfb9473810ce06c23595e7cba2ad9edec29b382fb6f68bfe3","source":{"kind":"arxiv","id":"1712.00575","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1712.00575","created_at":"2026-05-18T00:29:04Z"},{"alias_kind":"arxiv_version","alias_value":"1712.00575v1","created_at":"2026-05-18T00:29:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1712.00575","created_at":"2026-05-18T00:29:04Z"},{"alias_kind":"pith_short_12","alias_value":"UVLHN6UGD454","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_16","alias_value":"UVLHN6UGD454Z64U","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_8","alias_value":"UVLHN6UG","created_at":"2026-05-18T12:31:49Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:UVLHN6UGD454Z64UOOAQZYDMEN","target":"record","payload":{"canonical_record":{"source":{"id":"1712.00575","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-12-02T09:08:05Z","cross_cats_sorted":[],"title_canon_sha256":"b814cbe9e92674c5df9473feee004d7acdcbc310bb944f31742a5034788dfa8c","abstract_canon_sha256":"152e8939708567685d7044201179e8efecffea67b2d7ac58709a6bfeb0324066"},"schema_version":"1.0"},"canonical_sha256":"a55676fa861f3bccfb9473810ce06c23595e7cba2ad9edec29b382fb6f68bfe3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:29:04.830379Z","signature_b64":"+IsWDFaOjylZ+rbsDxuXuTzBRfA1gFINVlW6bLakk6OxxzBaPnzJAsmuM35v01LbScIkZvxD1B0dqLBUOxGzCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a55676fa861f3bccfb9473810ce06c23595e7cba2ad9edec29b382fb6f68bfe3","last_reissued_at":"2026-05-18T00:29:04.829650Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:29:04.829650Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1712.00575","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":"2026-05-18T00:29:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"loVViwOVlZmJEHm+qSTZWq+IOT2mAS8TDIXnZFutj2/6E7AX8ogix+S93Z5Bth44zYk3NaTzeckNAVMjHGpaCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T16:52:21.805147Z"},"content_sha256":"3e68b3bf276f8c696ec53a963c213d27938ea9fe2be212b8ba62a4f830ee4751","schema_version":"1.0","event_id":"sha256:3e68b3bf276f8c696ec53a963c213d27938ea9fe2be212b8ba62a4f830ee4751"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:UVLHN6UGD454Z64UOOAQZYDMEN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Lecture video indexing using boosted margin maximizing neural networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Di Ma, Gady Agam, Xi Zhang, Xu Ouyang","submitted_at":"2017-12-02T09:08:05Z","abstract_excerpt":"This paper presents a novel approach for lecture video indexing using a boosted deep convolutional neural network system. The indexing is performed by matching high quality slide images, for which text is either known or extracted, to lower resolution video frames with possible noise, perspective distortion, and occlusions. We propose a deep neural network integrated with a boosting framework composed of two sub-networks targeting feature extraction and similarity determination to perform the matching. The trained network is given as input a pair of slide image and a candidate video frame imag"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1712.00575","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":"2026-05-18T00:29:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zMr7zVDvCsL2GWNETrzKQ1gsmMFDSptU/6YyrbLWO0NBujruOUnBnZsJkyoDxRNfREbxyGp3CYZNBzdLqF40Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T16:52:21.805876Z"},"content_sha256":"225d186c40414996e4c4a97a28da190c57f50729b5c35af8e0f60886209ee2f0","schema_version":"1.0","event_id":"sha256:225d186c40414996e4c4a97a28da190c57f50729b5c35af8e0f60886209ee2f0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UVLHN6UGD454Z64UOOAQZYDMEN/bundle.json","state_url":"https://pith.science/pith/UVLHN6UGD454Z64UOOAQZYDMEN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UVLHN6UGD454Z64UOOAQZYDMEN/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":"2026-05-28T16:52:21Z","links":{"resolver":"https://pith.science/pith/UVLHN6UGD454Z64UOOAQZYDMEN","bundle":"https://pith.science/pith/UVLHN6UGD454Z64UOOAQZYDMEN/bundle.json","state":"https://pith.science/pith/UVLHN6UGD454Z64UOOAQZYDMEN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UVLHN6UGD454Z64UOOAQZYDMEN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:UVLHN6UGD454Z64UOOAQZYDMEN","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":"152e8939708567685d7044201179e8efecffea67b2d7ac58709a6bfeb0324066","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-12-02T09:08:05Z","title_canon_sha256":"b814cbe9e92674c5df9473feee004d7acdcbc310bb944f31742a5034788dfa8c"},"schema_version":"1.0","source":{"id":"1712.00575","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1712.00575","created_at":"2026-05-18T00:29:04Z"},{"alias_kind":"arxiv_version","alias_value":"1712.00575v1","created_at":"2026-05-18T00:29:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1712.00575","created_at":"2026-05-18T00:29:04Z"},{"alias_kind":"pith_short_12","alias_value":"UVLHN6UGD454","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_16","alias_value":"UVLHN6UGD454Z64U","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_8","alias_value":"UVLHN6UG","created_at":"2026-05-18T12:31:49Z"}],"graph_snapshots":[{"event_id":"sha256:225d186c40414996e4c4a97a28da190c57f50729b5c35af8e0f60886209ee2f0","target":"graph","created_at":"2026-05-18T00:29:04Z","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":"This paper presents a novel approach for lecture video indexing using a boosted deep convolutional neural network system. The indexing is performed by matching high quality slide images, for which text is either known or extracted, to lower resolution video frames with possible noise, perspective distortion, and occlusions. We propose a deep neural network integrated with a boosting framework composed of two sub-networks targeting feature extraction and similarity determination to perform the matching. The trained network is given as input a pair of slide image and a candidate video frame imag","authors_text":"Di Ma, Gady Agam, Xi Zhang, Xu Ouyang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-12-02T09:08:05Z","title":"Lecture video indexing using boosted margin maximizing neural networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1712.00575","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":"sha256:3e68b3bf276f8c696ec53a963c213d27938ea9fe2be212b8ba62a4f830ee4751","target":"record","created_at":"2026-05-18T00:29:04Z","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":"152e8939708567685d7044201179e8efecffea67b2d7ac58709a6bfeb0324066","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-12-02T09:08:05Z","title_canon_sha256":"b814cbe9e92674c5df9473feee004d7acdcbc310bb944f31742a5034788dfa8c"},"schema_version":"1.0","source":{"id":"1712.00575","kind":"arxiv","version":1}},"canonical_sha256":"a55676fa861f3bccfb9473810ce06c23595e7cba2ad9edec29b382fb6f68bfe3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a55676fa861f3bccfb9473810ce06c23595e7cba2ad9edec29b382fb6f68bfe3","first_computed_at":"2026-05-18T00:29:04.829650Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:29:04.829650Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"+IsWDFaOjylZ+rbsDxuXuTzBRfA1gFINVlW6bLakk6OxxzBaPnzJAsmuM35v01LbScIkZvxD1B0dqLBUOxGzCg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:29:04.830379Z","signed_message":"canonical_sha256_bytes"},"source_id":"1712.00575","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3e68b3bf276f8c696ec53a963c213d27938ea9fe2be212b8ba62a4f830ee4751","sha256:225d186c40414996e4c4a97a28da190c57f50729b5c35af8e0f60886209ee2f0"],"state_sha256":"7ecdd7216aaa1f06cb7ae0cd1820f0fb880ea7624917e217bd2acf10f9a95ac5"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oRROxY8zTbIPoi1CUXNFeB72K68IYKxrDyW/UOChCKh5KsLJ7H2yhAToIGR3g4JoIiRdEs5FAGMwyP6kcOSHBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T16:52:21.809622Z","bundle_sha256":"f5cc3baab2a5a0f1a91b4d2b0553058fb45e6ee6d2cca39fdff7fc0cb61d61aa"}}