{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:TF6QC4MMA2N47YQ7TACRBSIV52","short_pith_number":"pith:TF6QC4MM","canonical_record":{"source":{"id":"1811.08927","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2018-11-21T20:02:33Z","cross_cats_sorted":["cs.CV","cs.MM","eess.SP"],"title_canon_sha256":"366485359f79cafd9766bbdaff98f1a60bf14383f850d08a81cec463d306a492","abstract_canon_sha256":"e061c3c65a488da326a8a705c1922b973aa6f65824cd7b3638c488f2a99bf499"},"schema_version":"1.0"},"canonical_sha256":"997d01718c069bcfe21f980510c915eeac93d3ecece274c2921ffe5bc506421d","source":{"kind":"arxiv","id":"1811.08927","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.08927","created_at":"2026-05-18T00:00:07Z"},{"alias_kind":"arxiv_version","alias_value":"1811.08927v1","created_at":"2026-05-18T00:00:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.08927","created_at":"2026-05-18T00:00:07Z"},{"alias_kind":"pith_short_12","alias_value":"TF6QC4MMA2N4","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_16","alias_value":"TF6QC4MMA2N47YQ7","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_8","alias_value":"TF6QC4MM","created_at":"2026-05-18T12:32:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:TF6QC4MMA2N47YQ7TACRBSIV52","target":"record","payload":{"canonical_record":{"source":{"id":"1811.08927","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2018-11-21T20:02:33Z","cross_cats_sorted":["cs.CV","cs.MM","eess.SP"],"title_canon_sha256":"366485359f79cafd9766bbdaff98f1a60bf14383f850d08a81cec463d306a492","abstract_canon_sha256":"e061c3c65a488da326a8a705c1922b973aa6f65824cd7b3638c488f2a99bf499"},"schema_version":"1.0"},"canonical_sha256":"997d01718c069bcfe21f980510c915eeac93d3ecece274c2921ffe5bc506421d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:00:07.585496Z","signature_b64":"0T5O8frIGHLKaBvJjYMkzHOchpckpuRSh7WvzPG7Ioixcva0dtzubGAvbXxecmolxamhy+h5CNVVMq9PHGLjAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"997d01718c069bcfe21f980510c915eeac93d3ecece274c2921ffe5bc506421d","last_reissued_at":"2026-05-18T00:00:07.584771Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:00:07.584771Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1811.08927","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":"2026-05-18T00:00:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yxWb5n2Rwh0S1Y3TaJcjJ5+nBCl/+Eg1JrwkESrULnxfnSnNHu17hMr1qIYwcdfhtVuhGvONvsXjhjnxkU6eDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T03:26:52.061689Z"},"content_sha256":"1341e872a6473df705f61aedcd5e8371dc6d1f2e2ba0215decbd55523f53ef64","schema_version":"1.0","event_id":"sha256:1341e872a6473df705f61aedcd5e8371dc6d1f2e2ba0215decbd55523f53ef64"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:TF6QC4MMA2N47YQ7TACRBSIV52","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Generating Adaptive and Robust Filter Sets Using an Unsupervised Learning Framework","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.MM","eess.SP"],"primary_cat":"eess.IV","authors_text":"Dogancan Temel, Ghassan AlRegib, Mohit Prabhushankar","submitted_at":"2018-11-21T20:02:33Z","abstract_excerpt":"In this paper, we introduce an adaptive unsupervised learning framework, which utilizes natural images to train filter sets. The applicability of these filter sets is demonstrated by evaluating their performance in two contrasting applications - image quality assessment and texture retrieval. While assessing image quality, the filters need to capture perceptual differences based on dissimilarities between a reference image and its distorted version. In texture retrieval, the filters need to assess similarity between texture images to retrieve closest matching textures. Based on experiments, we"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.08927","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":"2026-05-18T00:00:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lP0mxAZQR1/3APUJmWDTnQf7cIO23iXNouMrBTIP0SnCd+Q94ZwdACW6/Pvhhy2Hlnh+iS1j7+NfdEVOR2y7Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T03:26:52.062036Z"},"content_sha256":"b64bb022b4ef28d014c3d150da855fa7d869a4051855e1b61a4dad36566804f7","schema_version":"1.0","event_id":"sha256:b64bb022b4ef28d014c3d150da855fa7d869a4051855e1b61a4dad36566804f7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TF6QC4MMA2N47YQ7TACRBSIV52/bundle.json","state_url":"https://pith.science/pith/TF6QC4MMA2N47YQ7TACRBSIV52/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TF6QC4MMA2N47YQ7TACRBSIV52/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":"2026-06-03T03:26:52Z","links":{"resolver":"https://pith.science/pith/TF6QC4MMA2N47YQ7TACRBSIV52","bundle":"https://pith.science/pith/TF6QC4MMA2N47YQ7TACRBSIV52/bundle.json","state":"https://pith.science/pith/TF6QC4MMA2N47YQ7TACRBSIV52/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TF6QC4MMA2N47YQ7TACRBSIV52/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:TF6QC4MMA2N47YQ7TACRBSIV52","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":"e061c3c65a488da326a8a705c1922b973aa6f65824cd7b3638c488f2a99bf499","cross_cats_sorted":["cs.CV","cs.MM","eess.SP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2018-11-21T20:02:33Z","title_canon_sha256":"366485359f79cafd9766bbdaff98f1a60bf14383f850d08a81cec463d306a492"},"schema_version":"1.0","source":{"id":"1811.08927","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.08927","created_at":"2026-05-18T00:00:07Z"},{"alias_kind":"arxiv_version","alias_value":"1811.08927v1","created_at":"2026-05-18T00:00:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.08927","created_at":"2026-05-18T00:00:07Z"},{"alias_kind":"pith_short_12","alias_value":"TF6QC4MMA2N4","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_16","alias_value":"TF6QC4MMA2N47YQ7","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_8","alias_value":"TF6QC4MM","created_at":"2026-05-18T12:32:53Z"}],"graph_snapshots":[{"event_id":"sha256:b64bb022b4ef28d014c3d150da855fa7d869a4051855e1b61a4dad36566804f7","target":"graph","created_at":"2026-05-18T00:00:07Z","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 this paper, we introduce an adaptive unsupervised learning framework, which utilizes natural images to train filter sets. The applicability of these filter sets is demonstrated by evaluating their performance in two contrasting applications - image quality assessment and texture retrieval. While assessing image quality, the filters need to capture perceptual differences based on dissimilarities between a reference image and its distorted version. In texture retrieval, the filters need to assess similarity between texture images to retrieve closest matching textures. Based on experiments, we","authors_text":"Dogancan Temel, Ghassan AlRegib, Mohit Prabhushankar","cross_cats":["cs.CV","cs.MM","eess.SP"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2018-11-21T20:02:33Z","title":"Generating Adaptive and Robust Filter Sets Using an Unsupervised Learning Framework"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.08927","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":"sha256:1341e872a6473df705f61aedcd5e8371dc6d1f2e2ba0215decbd55523f53ef64","target":"record","created_at":"2026-05-18T00:00:07Z","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":"e061c3c65a488da326a8a705c1922b973aa6f65824cd7b3638c488f2a99bf499","cross_cats_sorted":["cs.CV","cs.MM","eess.SP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2018-11-21T20:02:33Z","title_canon_sha256":"366485359f79cafd9766bbdaff98f1a60bf14383f850d08a81cec463d306a492"},"schema_version":"1.0","source":{"id":"1811.08927","kind":"arxiv","version":1}},"canonical_sha256":"997d01718c069bcfe21f980510c915eeac93d3ecece274c2921ffe5bc506421d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"997d01718c069bcfe21f980510c915eeac93d3ecece274c2921ffe5bc506421d","first_computed_at":"2026-05-18T00:00:07.584771Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:00:07.584771Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"0T5O8frIGHLKaBvJjYMkzHOchpckpuRSh7WvzPG7Ioixcva0dtzubGAvbXxecmolxamhy+h5CNVVMq9PHGLjAg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:00:07.585496Z","signed_message":"canonical_sha256_bytes"},"source_id":"1811.08927","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1341e872a6473df705f61aedcd5e8371dc6d1f2e2ba0215decbd55523f53ef64","sha256:b64bb022b4ef28d014c3d150da855fa7d869a4051855e1b61a4dad36566804f7"],"state_sha256":"5087b519e0d51e73069d9aa667c70162f7f5f70e8ea3070c8f6cbe7adc6b9893"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yFug2ICoQEL1xHSdE7HViOF/FGDNglCTtC+QhbDdjpqiH0UFHWspnOuArmdjl5R6Uz28pUJm7d7h2HFvhqIZDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-03T03:26:52.063982Z","bundle_sha256":"9bf0d557d0b337d4b14f10ce79054fe78328afdba0abbd37399d1c378632f500"}}