{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:IJ5BVNLK75ZY2LBJ2GYVCF4MPH","short_pith_number":"pith:IJ5BVNLK","schema_version":"1.0","canonical_sha256":"427a1ab56aff738d2c29d1b151178c79df0828f9c8e8fcb9721ffb80ced4cf33","source":{"kind":"arxiv","id":"1505.00855","version":1},"attestation_state":"computed","paper":{"title":"Large-scale Classification of Fine-Art Paintings: Learning The Right Metric on The Right Feature","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR","cs.LG","cs.MM"],"primary_cat":"cs.CV","authors_text":"Ahmed Elgammal, Babak Saleh","submitted_at":"2015-05-05T01:25:26Z","abstract_excerpt":"In the past few years, the number of fine-art collections that are digitized and publicly available has been growing rapidly. With the availability of such large collections of digitized artworks comes the need to develop multimedia systems to archive and retrieve this pool of data. Measuring the visual similarity between artistic items is an essential step for such multimedia systems, which can benefit more high-level multimedia tasks. In order to model this similarity between paintings, we should extract the appropriate visual features for paintings and find out the best approach to learn th"},"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":"1505.00855","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-05-05T01:25:26Z","cross_cats_sorted":["cs.IR","cs.LG","cs.MM"],"title_canon_sha256":"00c0876abbeb32d52d78cd28a9b715de0fe302cd05dbedc1d4d7bd72746ccd1b","abstract_canon_sha256":"c2d7db936a1c7255a74034bf64f18d9a92fcc7c46b68f400e4f7b2359bf15651"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:16:59.824481Z","signature_b64":"YvDNr8iFqywl2XRmlQ0vKZDrJQV+w5/ixT93rGo3pgVtweLHI23zc5GOmTEzsPYKApgpkHRn4WyqZRbRDUFqAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"427a1ab56aff738d2c29d1b151178c79df0828f9c8e8fcb9721ffb80ced4cf33","last_reissued_at":"2026-05-18T02:16:59.823676Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:16:59.823676Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Large-scale Classification of Fine-Art Paintings: Learning The Right Metric on The Right Feature","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR","cs.LG","cs.MM"],"primary_cat":"cs.CV","authors_text":"Ahmed Elgammal, Babak Saleh","submitted_at":"2015-05-05T01:25:26Z","abstract_excerpt":"In the past few years, the number of fine-art collections that are digitized and publicly available has been growing rapidly. With the availability of such large collections of digitized artworks comes the need to develop multimedia systems to archive and retrieve this pool of data. Measuring the visual similarity between artistic items is an essential step for such multimedia systems, which can benefit more high-level multimedia tasks. In order to model this similarity between paintings, we should extract the appropriate visual features for paintings and find out the best approach to learn th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1505.00855","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1505.00855","created_at":"2026-05-18T02:16:59.823805+00:00"},{"alias_kind":"arxiv_version","alias_value":"1505.00855v1","created_at":"2026-05-18T02:16:59.823805+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1505.00855","created_at":"2026-05-18T02:16:59.823805+00:00"},{"alias_kind":"pith_short_12","alias_value":"IJ5BVNLK75ZY","created_at":"2026-05-18T12:29:25.134429+00:00"},{"alias_kind":"pith_short_16","alias_value":"IJ5BVNLK75ZY2LBJ","created_at":"2026-05-18T12:29:25.134429+00:00"},{"alias_kind":"pith_short_8","alias_value":"IJ5BVNLK","created_at":"2026-05-18T12:29:25.134429+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":10,"internal_anchor_count":7,"sample":[{"citing_arxiv_id":"1907.00274","citing_title":"NetTailor: Tuning the Architecture, Not Just the Weights","ref_index":56,"is_internal_anchor":true},{"citing_arxiv_id":"1907.03537","citing_title":"Linking Art through Human Poses","ref_index":8,"is_internal_anchor":true},{"citing_arxiv_id":"2507.23313","citing_title":"The Cow of Rembrandt - Analyzing Artistic Prompt Interpretation in Text-to-Image Models","ref_index":7,"is_internal_anchor":true},{"citing_arxiv_id":"2605.20674","citing_title":"Modular Multimodal Classification Without Fine-Tuning: A Simple Compositional Approach","ref_index":24,"is_internal_anchor":true},{"citing_arxiv_id":"2509.19207","citing_title":"Long Story Short: Disentangling Compositionality and Long-Caption Understanding in Contrastive VLMs","ref_index":36,"is_internal_anchor":true},{"citing_arxiv_id":"2511.15197","citing_title":"Insert In Style: A Zero-Shot Generative Framework for Harmonious Cross-Domain Object Composition","ref_index":41,"is_internal_anchor":true},{"citing_arxiv_id":"2601.09896","citing_title":"The Algorithmic Gaze of Image Quality Assessment: An Audit and Trace Ethnography of the LAION-Aesthetics Predictor","ref_index":92,"is_internal_anchor":true},{"citing_arxiv_id":"2311.12793","citing_title":"ShareGPT4V: Improving Large Multi-Modal Models with Better Captions","ref_index":47,"is_internal_anchor":false},{"citing_arxiv_id":"2604.25119","citing_title":"Evaluation without Generation: Non-Generative Assessment of Harmful Model Specialization with Applications to CSAM","ref_index":46,"is_internal_anchor":false},{"citing_arxiv_id":"2412.10302","citing_title":"DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding","ref_index":74,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IJ5BVNLK75ZY2LBJ2GYVCF4MPH","json":"https://pith.science/pith/IJ5BVNLK75ZY2LBJ2GYVCF4MPH.json","graph_json":"https://pith.science/api/pith-number/IJ5BVNLK75ZY2LBJ2GYVCF4MPH/graph.json","events_json":"https://pith.science/api/pith-number/IJ5BVNLK75ZY2LBJ2GYVCF4MPH/events.json","paper":"https://pith.science/paper/IJ5BVNLK"},"agent_actions":{"view_html":"https://pith.science/pith/IJ5BVNLK75ZY2LBJ2GYVCF4MPH","download_json":"https://pith.science/pith/IJ5BVNLK75ZY2LBJ2GYVCF4MPH.json","view_paper":"https://pith.science/paper/IJ5BVNLK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1505.00855&json=true","fetch_graph":"https://pith.science/api/pith-number/IJ5BVNLK75ZY2LBJ2GYVCF4MPH/graph.json","fetch_events":"https://pith.science/api/pith-number/IJ5BVNLK75ZY2LBJ2GYVCF4MPH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IJ5BVNLK75ZY2LBJ2GYVCF4MPH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IJ5BVNLK75ZY2LBJ2GYVCF4MPH/action/storage_attestation","attest_author":"https://pith.science/pith/IJ5BVNLK75ZY2LBJ2GYVCF4MPH/action/author_attestation","sign_citation":"https://pith.science/pith/IJ5BVNLK75ZY2LBJ2GYVCF4MPH/action/citation_signature","submit_replication":"https://pith.science/pith/IJ5BVNLK75ZY2LBJ2GYVCF4MPH/action/replication_record"}},"created_at":"2026-05-18T02:16:59.823805+00:00","updated_at":"2026-05-18T02:16:59.823805+00:00"}