{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2013:RBYOBAYX5MKGUQOOXJXAXZ7KH3","short_pith_number":"pith:RBYOBAYX","canonical_record":{"source":{"id":"1310.5767","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2013-10-22T00:38:59Z","cross_cats_sorted":[],"title_canon_sha256":"ec9d90ecbfac6550f1d471faa48d74907548fad19eb1bd78162fe0af90f0780c","abstract_canon_sha256":"14ae97ca3f64b8dd1c94ab3bbf24777e8b1c9039ef7b275428e839a495a456f5"},"schema_version":"1.0"},"canonical_sha256":"8870e08317eb146a41ceba6e0be7ea3efdef90e20944271389cc30d7ebb41407","source":{"kind":"arxiv","id":"1310.5767","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1310.5767","created_at":"2026-05-18T03:09:32Z"},{"alias_kind":"arxiv_version","alias_value":"1310.5767v1","created_at":"2026-05-18T03:09:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1310.5767","created_at":"2026-05-18T03:09:32Z"},{"alias_kind":"pith_short_12","alias_value":"RBYOBAYX5MKG","created_at":"2026-05-18T12:27:57Z"},{"alias_kind":"pith_short_16","alias_value":"RBYOBAYX5MKGUQOO","created_at":"2026-05-18T12:27:57Z"},{"alias_kind":"pith_short_8","alias_value":"RBYOBAYX","created_at":"2026-05-18T12:27:57Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2013:RBYOBAYX5MKGUQOOXJXAXZ7KH3","target":"record","payload":{"canonical_record":{"source":{"id":"1310.5767","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2013-10-22T00:38:59Z","cross_cats_sorted":[],"title_canon_sha256":"ec9d90ecbfac6550f1d471faa48d74907548fad19eb1bd78162fe0af90f0780c","abstract_canon_sha256":"14ae97ca3f64b8dd1c94ab3bbf24777e8b1c9039ef7b275428e839a495a456f5"},"schema_version":"1.0"},"canonical_sha256":"8870e08317eb146a41ceba6e0be7ea3efdef90e20944271389cc30d7ebb41407","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:09:32.430994Z","signature_b64":"Yj+Tn6LOHGih+xkbH3lKMoJJ1PMyP9l5p4xBB8IkhJWnKEQx6vX3W3wxTA0jgw0uyx9EWEX4wztAk3oJyi7WAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8870e08317eb146a41ceba6e0be7ea3efdef90e20944271389cc30d7ebb41407","last_reissued_at":"2026-05-18T03:09:32.430096Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:09:32.430096Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1310.5767","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":"2026-05-18T03:09:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"M5LdAcdL3z9GSyM912vQX65bNukZC/lJerLYtW5aeXUK7otZcQElqZuZZstqgFv0w1zipCqN2c37R/f2rsP0Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T16:07:17.110102Z"},"content_sha256":"c3ced1b81ccc89d5211a511669ec124757c9c7365f364087812c3faa51ae3302","schema_version":"1.0","event_id":"sha256:c3ced1b81ccc89d5211a511669ec124757c9c7365f364087812c3faa51ae3302"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2013:RBYOBAYX5MKGUQOOXJXAXZ7KH3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Contextual Hypergraph Modelling for Salient Object Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Anthony Dick, Anton van den Hengel, Chunhua Shen, Xi Li, Yao Li","submitted_at":"2013-10-22T00:38:59Z","abstract_excerpt":"Salient object detection aims to locate objects that capture human attention within images. Previous approaches often pose this as a problem of image contrast analysis. In this work, we model an image as a hypergraph that utilizes a set of hyperedges to capture the contextual properties of image pixels or regions. As a result, the problem of salient object detection becomes one of finding salient vertices and hyperedges in the hypergraph. The main advantage of hypergraph modeling is that it takes into account each pixel's (or region's) affinity with its neighborhood as well as its separation f"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1310.5767","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":"2026-05-18T03:09:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3anUZ/t2JvhhYHja7mZhVWDrMqReqwJ8DdEytud7ZMXu3Vtaq7i85xMtXoV4FOp6HPLMYywhj1OZ4rCm9VbJAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T16:07:17.110540Z"},"content_sha256":"c83aa83f90a3ec6612cc37626ad0e3e022096c92f449af4ee4c803ee9dd19761","schema_version":"1.0","event_id":"sha256:c83aa83f90a3ec6612cc37626ad0e3e022096c92f449af4ee4c803ee9dd19761"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RBYOBAYX5MKGUQOOXJXAXZ7KH3/bundle.json","state_url":"https://pith.science/pith/RBYOBAYX5MKGUQOOXJXAXZ7KH3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RBYOBAYX5MKGUQOOXJXAXZ7KH3/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":"2026-05-26T16:07:17Z","links":{"resolver":"https://pith.science/pith/RBYOBAYX5MKGUQOOXJXAXZ7KH3","bundle":"https://pith.science/pith/RBYOBAYX5MKGUQOOXJXAXZ7KH3/bundle.json","state":"https://pith.science/pith/RBYOBAYX5MKGUQOOXJXAXZ7KH3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RBYOBAYX5MKGUQOOXJXAXZ7KH3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2013:RBYOBAYX5MKGUQOOXJXAXZ7KH3","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":"14ae97ca3f64b8dd1c94ab3bbf24777e8b1c9039ef7b275428e839a495a456f5","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2013-10-22T00:38:59Z","title_canon_sha256":"ec9d90ecbfac6550f1d471faa48d74907548fad19eb1bd78162fe0af90f0780c"},"schema_version":"1.0","source":{"id":"1310.5767","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1310.5767","created_at":"2026-05-18T03:09:32Z"},{"alias_kind":"arxiv_version","alias_value":"1310.5767v1","created_at":"2026-05-18T03:09:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1310.5767","created_at":"2026-05-18T03:09:32Z"},{"alias_kind":"pith_short_12","alias_value":"RBYOBAYX5MKG","created_at":"2026-05-18T12:27:57Z"},{"alias_kind":"pith_short_16","alias_value":"RBYOBAYX5MKGUQOO","created_at":"2026-05-18T12:27:57Z"},{"alias_kind":"pith_short_8","alias_value":"RBYOBAYX","created_at":"2026-05-18T12:27:57Z"}],"graph_snapshots":[{"event_id":"sha256:c83aa83f90a3ec6612cc37626ad0e3e022096c92f449af4ee4c803ee9dd19761","target":"graph","created_at":"2026-05-18T03:09:32Z","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":"Salient object detection aims to locate objects that capture human attention within images. Previous approaches often pose this as a problem of image contrast analysis. In this work, we model an image as a hypergraph that utilizes a set of hyperedges to capture the contextual properties of image pixels or regions. As a result, the problem of salient object detection becomes one of finding salient vertices and hyperedges in the hypergraph. The main advantage of hypergraph modeling is that it takes into account each pixel's (or region's) affinity with its neighborhood as well as its separation f","authors_text":"Anthony Dick, Anton van den Hengel, Chunhua Shen, Xi Li, Yao Li","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2013-10-22T00:38:59Z","title":"Contextual Hypergraph Modelling for Salient Object Detection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1310.5767","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:c3ced1b81ccc89d5211a511669ec124757c9c7365f364087812c3faa51ae3302","target":"record","created_at":"2026-05-18T03:09:32Z","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":"14ae97ca3f64b8dd1c94ab3bbf24777e8b1c9039ef7b275428e839a495a456f5","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2013-10-22T00:38:59Z","title_canon_sha256":"ec9d90ecbfac6550f1d471faa48d74907548fad19eb1bd78162fe0af90f0780c"},"schema_version":"1.0","source":{"id":"1310.5767","kind":"arxiv","version":1}},"canonical_sha256":"8870e08317eb146a41ceba6e0be7ea3efdef90e20944271389cc30d7ebb41407","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8870e08317eb146a41ceba6e0be7ea3efdef90e20944271389cc30d7ebb41407","first_computed_at":"2026-05-18T03:09:32.430096Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:09:32.430096Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Yj+Tn6LOHGih+xkbH3lKMoJJ1PMyP9l5p4xBB8IkhJWnKEQx6vX3W3wxTA0jgw0uyx9EWEX4wztAk3oJyi7WAQ==","signature_status":"signed_v1","signed_at":"2026-05-18T03:09:32.430994Z","signed_message":"canonical_sha256_bytes"},"source_id":"1310.5767","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c3ced1b81ccc89d5211a511669ec124757c9c7365f364087812c3faa51ae3302","sha256:c83aa83f90a3ec6612cc37626ad0e3e022096c92f449af4ee4c803ee9dd19761"],"state_sha256":"2edd28b3d965b7ea2ba318a792998249aa9ddfdc42ae9296bead913d2d02bc58"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UCHFT7BUBvRcIOFJJb6ZcDEJbm0xkSvADkoeGbPcDsGC736kzDeaBAd/bMKik7RvlyYxu97QtepR9FA7fLqUAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T16:07:17.112978Z","bundle_sha256":"9c8243efc5f20abc7075407ff75d6732c3a85c54caeb130f54b87cbb74abcada"}}