{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:2EYYKAJLMMFLQ527EPOTZFGVFO","short_pith_number":"pith:2EYYKAJL","schema_version":"1.0","canonical_sha256":"d13185012b630ab8775f23dd3c94d52b830276298fe17201974473973c6eb2f5","source":{"kind":"arxiv","id":"1908.08505","version":1},"attestation_state":"computed","paper":{"title":"ColorNet -- Estimating Colorfulness in Natural Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR","cs.LG","eess.IV"],"primary_cat":"cs.MM","authors_text":"Aakanksha Rana, Aljosa Smolic, Emin Zerman","submitted_at":"2019-08-22T17:24:37Z","abstract_excerpt":"Measuring the colorfulness of a natural or virtual scene is critical for many applications in image processing field ranging from capturing to display. In this paper, we propose the first deep learning-based colorfulness estimation metric. For this purpose, we develop a color rating model which simultaneously learns to extracts the pertinent characteristic color features and the mapping from feature space to the ideal colorfulness scores for a variety of natural colored images. Additionally, we propose to overcome the lack of adequate annotated dataset problem by combining/aligning two publicl"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1908.08505","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MM","submitted_at":"2019-08-22T17:24:37Z","cross_cats_sorted":["cs.GR","cs.LG","eess.IV"],"title_canon_sha256":"2925c47eedca2695038027639835946d188fc3cdac66e2e8c694e8502cd996e6","abstract_canon_sha256":"304fcb18740e4c52e9f88a88f559c2a6f8f3833a530b792b193f54d903d4f15c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-04T23:59:13.558768Z","signature_b64":"Is6GPU/l6itqjV3o9yU/PWApX/mlBL8yTnOplWzQTcL4i7ZlYl/jUtrSlsdzdK6+7imeQ3WtvFie8D1/uwj2Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d13185012b630ab8775f23dd3c94d52b830276298fe17201974473973c6eb2f5","last_reissued_at":"2026-07-04T23:59:13.558365Z","signature_status":"signed_v1","first_computed_at":"2026-07-04T23:59:13.558365Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ColorNet -- Estimating Colorfulness in Natural Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR","cs.LG","eess.IV"],"primary_cat":"cs.MM","authors_text":"Aakanksha Rana, Aljosa Smolic, Emin Zerman","submitted_at":"2019-08-22T17:24:37Z","abstract_excerpt":"Measuring the colorfulness of a natural or virtual scene is critical for many applications in image processing field ranging from capturing to display. In this paper, we propose the first deep learning-based colorfulness estimation metric. For this purpose, we develop a color rating model which simultaneously learns to extracts the pertinent characteristic color features and the mapping from feature space to the ideal colorfulness scores for a variety of natural colored images. Additionally, we propose to overcome the lack of adequate annotated dataset problem by combining/aligning two publicl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.08505","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1908.08505/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1908.08505","created_at":"2026-07-04T23:59:13.558423+00:00"},{"alias_kind":"arxiv_version","alias_value":"1908.08505v1","created_at":"2026-07-04T23:59:13.558423+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.08505","created_at":"2026-07-04T23:59:13.558423+00:00"},{"alias_kind":"pith_short_12","alias_value":"2EYYKAJLMMFL","created_at":"2026-07-04T23:59:13.558423+00:00"},{"alias_kind":"pith_short_16","alias_value":"2EYYKAJLMMFLQ527","created_at":"2026-07-04T23:59:13.558423+00:00"},{"alias_kind":"pith_short_8","alias_value":"2EYYKAJL","created_at":"2026-07-04T23:59:13.558423+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1908.08505","citing_title":"ColorNet -- Estimating Colorfulness in Natural Images","ref_index":2,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2EYYKAJLMMFLQ527EPOTZFGVFO","json":"https://pith.science/pith/2EYYKAJLMMFLQ527EPOTZFGVFO.json","graph_json":"https://pith.science/api/pith-number/2EYYKAJLMMFLQ527EPOTZFGVFO/graph.json","events_json":"https://pith.science/api/pith-number/2EYYKAJLMMFLQ527EPOTZFGVFO/events.json","paper":"https://pith.science/paper/2EYYKAJL"},"agent_actions":{"view_html":"https://pith.science/pith/2EYYKAJLMMFLQ527EPOTZFGVFO","download_json":"https://pith.science/pith/2EYYKAJLMMFLQ527EPOTZFGVFO.json","view_paper":"https://pith.science/paper/2EYYKAJL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1908.08505&json=true","fetch_graph":"https://pith.science/api/pith-number/2EYYKAJLMMFLQ527EPOTZFGVFO/graph.json","fetch_events":"https://pith.science/api/pith-number/2EYYKAJLMMFLQ527EPOTZFGVFO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2EYYKAJLMMFLQ527EPOTZFGVFO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2EYYKAJLMMFLQ527EPOTZFGVFO/action/storage_attestation","attest_author":"https://pith.science/pith/2EYYKAJLMMFLQ527EPOTZFGVFO/action/author_attestation","sign_citation":"https://pith.science/pith/2EYYKAJLMMFLQ527EPOTZFGVFO/action/citation_signature","submit_replication":"https://pith.science/pith/2EYYKAJLMMFLQ527EPOTZFGVFO/action/replication_record"}},"created_at":"2026-07-04T23:59:13.558423+00:00","updated_at":"2026-07-04T23:59:13.558423+00:00"}