{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:DDTNLYHA3FVDJEUX5ZKHS7K2UF","short_pith_number":"pith:DDTNLYHA","schema_version":"1.0","canonical_sha256":"18e6d5e0e0d96a349297ee54797d5aa144d0f2471e438df43e3d0d1ce320a077","source":{"kind":"arxiv","id":"1608.07664","version":1},"attestation_state":"computed","paper":{"title":"Spatio-temporal Aware Non-negative Component Representation for Action Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jianhong Wang, Limin Luo, Tian Lan, Xu Zhang","submitted_at":"2016-08-27T06:30:34Z","abstract_excerpt":"This paper presents a novel mid-level representation for action recognition, named spatio-temporal aware non-negative component representation (STANNCR). The proposed STANNCR is based on action component and incorporates the spatial-temporal information. We first introduce a spatial-temporal distribution vector (STDV) to model the distributions of local feature locations in a compact and discriminative manner. Then we employ non-negative matrix factorization (NMF) to learn the action components and encode the video samples. The action component considers the correlations of visual words, which"},"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":"1608.07664","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-08-27T06:30:34Z","cross_cats_sorted":[],"title_canon_sha256":"441578b6c8aa7f1567a7cab20ca800e83104cc5ff27e544cbd2677c9ab567a44","abstract_canon_sha256":"bfcd7010694eb66b88f35679ff728f77b86b57a45f840759dcce8da6391545da"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:07:50.161456Z","signature_b64":"d5xND7ksRgMKPbg9ZlPRFiUdgrd3ZNod8cThs3C/SnSci4UqT9lPZ+R1A0jR5294oFCwv1KY025GsMNgAPlgDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"18e6d5e0e0d96a349297ee54797d5aa144d0f2471e438df43e3d0d1ce320a077","last_reissued_at":"2026-05-18T01:07:50.161063Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:07:50.161063Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Spatio-temporal Aware Non-negative Component Representation for Action Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jianhong Wang, Limin Luo, Tian Lan, Xu Zhang","submitted_at":"2016-08-27T06:30:34Z","abstract_excerpt":"This paper presents a novel mid-level representation for action recognition, named spatio-temporal aware non-negative component representation (STANNCR). The proposed STANNCR is based on action component and incorporates the spatial-temporal information. We first introduce a spatial-temporal distribution vector (STDV) to model the distributions of local feature locations in a compact and discriminative manner. Then we employ non-negative matrix factorization (NMF) to learn the action components and encode the video samples. The action component considers the correlations of visual words, which"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1608.07664","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":"1608.07664","created_at":"2026-05-18T01:07:50.161112+00:00"},{"alias_kind":"arxiv_version","alias_value":"1608.07664v1","created_at":"2026-05-18T01:07:50.161112+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1608.07664","created_at":"2026-05-18T01:07:50.161112+00:00"},{"alias_kind":"pith_short_12","alias_value":"DDTNLYHA3FVD","created_at":"2026-05-18T12:30:12.583610+00:00"},{"alias_kind":"pith_short_16","alias_value":"DDTNLYHA3FVDJEUX","created_at":"2026-05-18T12:30:12.583610+00:00"},{"alias_kind":"pith_short_8","alias_value":"DDTNLYHA","created_at":"2026-05-18T12:30:12.583610+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DDTNLYHA3FVDJEUX5ZKHS7K2UF","json":"https://pith.science/pith/DDTNLYHA3FVDJEUX5ZKHS7K2UF.json","graph_json":"https://pith.science/api/pith-number/DDTNLYHA3FVDJEUX5ZKHS7K2UF/graph.json","events_json":"https://pith.science/api/pith-number/DDTNLYHA3FVDJEUX5ZKHS7K2UF/events.json","paper":"https://pith.science/paper/DDTNLYHA"},"agent_actions":{"view_html":"https://pith.science/pith/DDTNLYHA3FVDJEUX5ZKHS7K2UF","download_json":"https://pith.science/pith/DDTNLYHA3FVDJEUX5ZKHS7K2UF.json","view_paper":"https://pith.science/paper/DDTNLYHA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1608.07664&json=true","fetch_graph":"https://pith.science/api/pith-number/DDTNLYHA3FVDJEUX5ZKHS7K2UF/graph.json","fetch_events":"https://pith.science/api/pith-number/DDTNLYHA3FVDJEUX5ZKHS7K2UF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DDTNLYHA3FVDJEUX5ZKHS7K2UF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DDTNLYHA3FVDJEUX5ZKHS7K2UF/action/storage_attestation","attest_author":"https://pith.science/pith/DDTNLYHA3FVDJEUX5ZKHS7K2UF/action/author_attestation","sign_citation":"https://pith.science/pith/DDTNLYHA3FVDJEUX5ZKHS7K2UF/action/citation_signature","submit_replication":"https://pith.science/pith/DDTNLYHA3FVDJEUX5ZKHS7K2UF/action/replication_record"}},"created_at":"2026-05-18T01:07:50.161112+00:00","updated_at":"2026-05-18T01:07:50.161112+00:00"}