{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:R5GPKUDD2TBM4UCN6BPOQFNV5Y","short_pith_number":"pith:R5GPKUDD","canonical_record":{"source":{"id":"1812.04816","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2018-12-12T05:50:06Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"287b47b48ea6d4c729cdde78cce565b07728143fc54d79db685748da293fb639","abstract_canon_sha256":"609d109460cb5a57226e62a714728bbf11d5102768617348bf21b0a2c1ce366b"},"schema_version":"1.0"},"canonical_sha256":"8f4cf55063d4c2ce504df05ee815b5ee059e86c3158db917e03cc9233c1ad445","source":{"kind":"arxiv","id":"1812.04816","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1812.04816","created_at":"2026-05-17T23:58:27Z"},{"alias_kind":"arxiv_version","alias_value":"1812.04816v1","created_at":"2026-05-17T23:58:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.04816","created_at":"2026-05-17T23:58:27Z"},{"alias_kind":"pith_short_12","alias_value":"R5GPKUDD2TBM","created_at":"2026-05-18T12:32:50Z"},{"alias_kind":"pith_short_16","alias_value":"R5GPKUDD2TBM4UCN","created_at":"2026-05-18T12:32:50Z"},{"alias_kind":"pith_short_8","alias_value":"R5GPKUDD","created_at":"2026-05-18T12:32:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:R5GPKUDD2TBM4UCN6BPOQFNV5Y","target":"record","payload":{"canonical_record":{"source":{"id":"1812.04816","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2018-12-12T05:50:06Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"287b47b48ea6d4c729cdde78cce565b07728143fc54d79db685748da293fb639","abstract_canon_sha256":"609d109460cb5a57226e62a714728bbf11d5102768617348bf21b0a2c1ce366b"},"schema_version":"1.0"},"canonical_sha256":"8f4cf55063d4c2ce504df05ee815b5ee059e86c3158db917e03cc9233c1ad445","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:58:27.266959Z","signature_b64":"EGnD0e5U5XZecZ//Ej4rD64C65ds8Vhc4mwOKaVDRDublLfnqcfbDH02AI77MAvnAAGAXO27h4hXFVVvrdNHAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8f4cf55063d4c2ce504df05ee815b5ee059e86c3158db917e03cc9233c1ad445","last_reissued_at":"2026-05-17T23:58:27.266224Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:58:27.266224Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1812.04816","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":"2026-05-17T23:58:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YUe8mq1UnD/eEC2AgDcAAXPUJ3DkX7/Q70whR+s41n9k5TEXIsJUkBuS6l7tK+wsNUMfv7sG+RMkW5hUmlMmAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T06:13:36.138064Z"},"content_sha256":"343eaae4363a308e56f4b1c6940079b75bdfb5769026dc7f813df7b4f7126f3f","schema_version":"1.0","event_id":"sha256:343eaae4363a308e56f4b1c6940079b75bdfb5769026dc7f813df7b4f7126f3f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:R5GPKUDD2TBM4UCN6BPOQFNV5Y","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Image Segmentation Based on Multiscale Fast Spectral Clustering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Chenjian Wu, Chongyang Zhang, Guofeng Zhu, Hong Chen, Minxin Chen","submitted_at":"2018-12-12T05:50:06Z","abstract_excerpt":"In recent years, spectral clustering has become one of the most popular clustering algorithms for image segmentation. However, it has restricted applicability to large-scale images due to its high computational complexity. In this paper, we first propose a novel algorithm called Fast Spectral Clustering based on quad-tree decomposition. The algorithm focuses on the spectral clustering at superpixel level and its computational complexity is O(nlogn) + O(m) + O(m^(3/2)); its memory cost is O(m), where n and m are the numbers of pixels and the superpixels of a image. Then we propose Multiscale Fa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.04816","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":"2026-05-17T23:58:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"M3xMPq9hxGP+QiParJDk7s7Pe7ENdjAngk+EQSwEpezuYGjTllPTKgO2FWeNLSI+W245uP/yj6nCbYxofarPDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T06:13:36.138753Z"},"content_sha256":"2092211c47cff65790b62d5def4a1d1fbbaa9e7a3bef89b61299facacbf88cb0","schema_version":"1.0","event_id":"sha256:2092211c47cff65790b62d5def4a1d1fbbaa9e7a3bef89b61299facacbf88cb0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/R5GPKUDD2TBM4UCN6BPOQFNV5Y/bundle.json","state_url":"https://pith.science/pith/R5GPKUDD2TBM4UCN6BPOQFNV5Y/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/R5GPKUDD2TBM4UCN6BPOQFNV5Y/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":"2026-05-27T06:13:36Z","links":{"resolver":"https://pith.science/pith/R5GPKUDD2TBM4UCN6BPOQFNV5Y","bundle":"https://pith.science/pith/R5GPKUDD2TBM4UCN6BPOQFNV5Y/bundle.json","state":"https://pith.science/pith/R5GPKUDD2TBM4UCN6BPOQFNV5Y/state.json","well_known_bundle":"https://pith.science/.well-known/pith/R5GPKUDD2TBM4UCN6BPOQFNV5Y/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:R5GPKUDD2TBM4UCN6BPOQFNV5Y","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":"609d109460cb5a57226e62a714728bbf11d5102768617348bf21b0a2c1ce366b","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2018-12-12T05:50:06Z","title_canon_sha256":"287b47b48ea6d4c729cdde78cce565b07728143fc54d79db685748da293fb639"},"schema_version":"1.0","source":{"id":"1812.04816","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1812.04816","created_at":"2026-05-17T23:58:27Z"},{"alias_kind":"arxiv_version","alias_value":"1812.04816v1","created_at":"2026-05-17T23:58:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.04816","created_at":"2026-05-17T23:58:27Z"},{"alias_kind":"pith_short_12","alias_value":"R5GPKUDD2TBM","created_at":"2026-05-18T12:32:50Z"},{"alias_kind":"pith_short_16","alias_value":"R5GPKUDD2TBM4UCN","created_at":"2026-05-18T12:32:50Z"},{"alias_kind":"pith_short_8","alias_value":"R5GPKUDD","created_at":"2026-05-18T12:32:50Z"}],"graph_snapshots":[{"event_id":"sha256:2092211c47cff65790b62d5def4a1d1fbbaa9e7a3bef89b61299facacbf88cb0","target":"graph","created_at":"2026-05-17T23:58:27Z","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":"In recent years, spectral clustering has become one of the most popular clustering algorithms for image segmentation. However, it has restricted applicability to large-scale images due to its high computational complexity. In this paper, we first propose a novel algorithm called Fast Spectral Clustering based on quad-tree decomposition. The algorithm focuses on the spectral clustering at superpixel level and its computational complexity is O(nlogn) + O(m) + O(m^(3/2)); its memory cost is O(m), where n and m are the numbers of pixels and the superpixels of a image. Then we propose Multiscale Fa","authors_text":"Chenjian Wu, Chongyang Zhang, Guofeng Zhu, Hong Chen, Minxin Chen","cross_cats":["cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2018-12-12T05:50:06Z","title":"Image Segmentation Based on Multiscale Fast Spectral Clustering"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.04816","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":"sha256:343eaae4363a308e56f4b1c6940079b75bdfb5769026dc7f813df7b4f7126f3f","target":"record","created_at":"2026-05-17T23:58:27Z","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":"609d109460cb5a57226e62a714728bbf11d5102768617348bf21b0a2c1ce366b","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2018-12-12T05:50:06Z","title_canon_sha256":"287b47b48ea6d4c729cdde78cce565b07728143fc54d79db685748da293fb639"},"schema_version":"1.0","source":{"id":"1812.04816","kind":"arxiv","version":1}},"canonical_sha256":"8f4cf55063d4c2ce504df05ee815b5ee059e86c3158db917e03cc9233c1ad445","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8f4cf55063d4c2ce504df05ee815b5ee059e86c3158db917e03cc9233c1ad445","first_computed_at":"2026-05-17T23:58:27.266224Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:58:27.266224Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"EGnD0e5U5XZecZ//Ej4rD64C65ds8Vhc4mwOKaVDRDublLfnqcfbDH02AI77MAvnAAGAXO27h4hXFVVvrdNHAA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:58:27.266959Z","signed_message":"canonical_sha256_bytes"},"source_id":"1812.04816","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:343eaae4363a308e56f4b1c6940079b75bdfb5769026dc7f813df7b4f7126f3f","sha256:2092211c47cff65790b62d5def4a1d1fbbaa9e7a3bef89b61299facacbf88cb0"],"state_sha256":"f1f8522653bc5c96a6ee648cd2d48c72573040eadc2451b57e57b0f87273384f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"v2oS+TH1sIUxJpsgCSo7u1IyfjbEa64fLeMDP6Q9bqJQ47sHPwpSOTS0Ub68/bkr0xhpc5FgNWjTppgxMTIkDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T06:13:36.142597Z","bundle_sha256":"e4bd52fbac596695539ceeee9dd2703b4d6e0851f69451702bd93062d6812c16"}}