{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:A5KIO3PII5USIDQZDMEHLR7KKP","short_pith_number":"pith:A5KIO3PI","canonical_record":{"source":{"id":"1506.03852","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-06-11T21:55:06Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"1ced7537aa42e6798975d738a249ed4b3dbb5977790ad64e278551abae77c884","abstract_canon_sha256":"fd00193a15d3fbf492aece4b8bebb53678f5fc0fc48eebca918d1646ed88929a"},"schema_version":"1.0"},"canonical_sha256":"0754876de84769240e191b0875c7ea53e195b4cb25ca60870b9ecb7da4661d75","source":{"kind":"arxiv","id":"1506.03852","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1506.03852","created_at":"2026-05-18T01:51:49Z"},{"alias_kind":"arxiv_version","alias_value":"1506.03852v1","created_at":"2026-05-18T01:51:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1506.03852","created_at":"2026-05-18T01:51:49Z"},{"alias_kind":"pith_short_12","alias_value":"A5KIO3PII5US","created_at":"2026-05-18T12:29:10Z"},{"alias_kind":"pith_short_16","alias_value":"A5KIO3PII5USIDQZ","created_at":"2026-05-18T12:29:10Z"},{"alias_kind":"pith_short_8","alias_value":"A5KIO3PI","created_at":"2026-05-18T12:29:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:A5KIO3PII5USIDQZDMEHLR7KKP","target":"record","payload":{"canonical_record":{"source":{"id":"1506.03852","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-06-11T21:55:06Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"1ced7537aa42e6798975d738a249ed4b3dbb5977790ad64e278551abae77c884","abstract_canon_sha256":"fd00193a15d3fbf492aece4b8bebb53678f5fc0fc48eebca918d1646ed88929a"},"schema_version":"1.0"},"canonical_sha256":"0754876de84769240e191b0875c7ea53e195b4cb25ca60870b9ecb7da4661d75","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:51:49.064284Z","signature_b64":"3fNYkWHfWAND3YTCfjweb8c1zI8P45lWm4pBY4lDkI6XIyulLoMulXF3OsosDvYF7fqZJHSF22x4C+0eNFU3CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0754876de84769240e191b0875c7ea53e195b4cb25ca60870b9ecb7da4661d75","last_reissued_at":"2026-05-18T01:51:49.063824Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:51:49.063824Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1506.03852","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":"2026-05-18T01:51:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8tTbeF5JxtVO77xj1oc6X4szOsx4XdInJ+y6YjFwj8X/yEVwthILu0ZbwYO7bB/kTtHPl2J31i/qkuKi3jGjCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-20T07:42:36.879733Z"},"content_sha256":"d64c4b31c563c0921b0927357e8bacf74e425b1d6fabb369e3a1c619ea4a9c80","schema_version":"1.0","event_id":"sha256:d64c4b31c563c0921b0927357e8bacf74e425b1d6fabb369e3a1c619ea4a9c80"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:A5KIO3PII5USIDQZDMEHLR7KKP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Tree-Cut for Probabilistic Image Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"stat.ML","authors_text":"Christopher K. I. Williams, Shell X. Hu, Sinisa Todorovic","submitted_at":"2015-06-11T21:55:06Z","abstract_excerpt":"This paper presents a new probabilistic generative model for image segmentation, i.e. the task of partitioning an image into homogeneous regions. Our model is grounded on a mid-level image representation, called a region tree, in which regions are recursively split into subregions until superpixels are reached. Given the region tree, image segmentation is formalized as sampling cuts in the tree from the model. Inference for the cuts is exact, and formulated using dynamic programming. Our tree-cut model can be tuned to sample segmentations at a particular scale of interest out of many possible "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1506.03852","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":"2026-05-18T01:51:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yAQYkNFqW0bxmtnWXlzRoUBL3pZ+VVXEyhpvaCbss0fiGyIYLy5MEbCQsmLrkPa+Ktf8Qq5i4PzQbFDK284vBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-20T07:42:36.880078Z"},"content_sha256":"b9acaf1c94674552ed759d8e9db9ab6bb483e3d900901e16786d4f451f3d8910","schema_version":"1.0","event_id":"sha256:b9acaf1c94674552ed759d8e9db9ab6bb483e3d900901e16786d4f451f3d8910"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/A5KIO3PII5USIDQZDMEHLR7KKP/bundle.json","state_url":"https://pith.science/pith/A5KIO3PII5USIDQZDMEHLR7KKP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/A5KIO3PII5USIDQZDMEHLR7KKP/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":"2026-06-20T07:42:36Z","links":{"resolver":"https://pith.science/pith/A5KIO3PII5USIDQZDMEHLR7KKP","bundle":"https://pith.science/pith/A5KIO3PII5USIDQZDMEHLR7KKP/bundle.json","state":"https://pith.science/pith/A5KIO3PII5USIDQZDMEHLR7KKP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/A5KIO3PII5USIDQZDMEHLR7KKP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:A5KIO3PII5USIDQZDMEHLR7KKP","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":"fd00193a15d3fbf492aece4b8bebb53678f5fc0fc48eebca918d1646ed88929a","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-06-11T21:55:06Z","title_canon_sha256":"1ced7537aa42e6798975d738a249ed4b3dbb5977790ad64e278551abae77c884"},"schema_version":"1.0","source":{"id":"1506.03852","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1506.03852","created_at":"2026-05-18T01:51:49Z"},{"alias_kind":"arxiv_version","alias_value":"1506.03852v1","created_at":"2026-05-18T01:51:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1506.03852","created_at":"2026-05-18T01:51:49Z"},{"alias_kind":"pith_short_12","alias_value":"A5KIO3PII5US","created_at":"2026-05-18T12:29:10Z"},{"alias_kind":"pith_short_16","alias_value":"A5KIO3PII5USIDQZ","created_at":"2026-05-18T12:29:10Z"},{"alias_kind":"pith_short_8","alias_value":"A5KIO3PI","created_at":"2026-05-18T12:29:10Z"}],"graph_snapshots":[{"event_id":"sha256:b9acaf1c94674552ed759d8e9db9ab6bb483e3d900901e16786d4f451f3d8910","target":"graph","created_at":"2026-05-18T01:51:49Z","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 new probabilistic generative model for image segmentation, i.e. the task of partitioning an image into homogeneous regions. Our model is grounded on a mid-level image representation, called a region tree, in which regions are recursively split into subregions until superpixels are reached. Given the region tree, image segmentation is formalized as sampling cuts in the tree from the model. Inference for the cuts is exact, and formulated using dynamic programming. Our tree-cut model can be tuned to sample segmentations at a particular scale of interest out of many possible ","authors_text":"Christopher K. I. Williams, Shell X. Hu, Sinisa Todorovic","cross_cats":["cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-06-11T21:55:06Z","title":"Tree-Cut for Probabilistic Image Segmentation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1506.03852","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:d64c4b31c563c0921b0927357e8bacf74e425b1d6fabb369e3a1c619ea4a9c80","target":"record","created_at":"2026-05-18T01:51:49Z","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":"fd00193a15d3fbf492aece4b8bebb53678f5fc0fc48eebca918d1646ed88929a","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-06-11T21:55:06Z","title_canon_sha256":"1ced7537aa42e6798975d738a249ed4b3dbb5977790ad64e278551abae77c884"},"schema_version":"1.0","source":{"id":"1506.03852","kind":"arxiv","version":1}},"canonical_sha256":"0754876de84769240e191b0875c7ea53e195b4cb25ca60870b9ecb7da4661d75","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0754876de84769240e191b0875c7ea53e195b4cb25ca60870b9ecb7da4661d75","first_computed_at":"2026-05-18T01:51:49.063824Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:51:49.063824Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"3fNYkWHfWAND3YTCfjweb8c1zI8P45lWm4pBY4lDkI6XIyulLoMulXF3OsosDvYF7fqZJHSF22x4C+0eNFU3CA==","signature_status":"signed_v1","signed_at":"2026-05-18T01:51:49.064284Z","signed_message":"canonical_sha256_bytes"},"source_id":"1506.03852","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d64c4b31c563c0921b0927357e8bacf74e425b1d6fabb369e3a1c619ea4a9c80","sha256:b9acaf1c94674552ed759d8e9db9ab6bb483e3d900901e16786d4f451f3d8910"],"state_sha256":"09eda003b10932fb1c5816794f6e3e0902f8c30c206ee412e1257c00275e3a4b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"C6sYTeZYZsoNtKQp3+pMTokuDp0qqQfe+MT8RcH2BgBoHYaDsv/Y98gAxe+itXNHNFvjy+jxxWoOiCOKWsRyCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-20T07:42:36.882039Z","bundle_sha256":"a950146bef50d04cf2cd943a6f8fcfdc34c8b23e2006c86b630b21ffc4af8c27"}}