{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:4PIO3SF2KJYFQSMLQW4X3VPH2C","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":"2d8888581c23371e81c0eb72b0a5316237e1904019bfda6b174d0fa945090342","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-06-15T21:19:54Z","title_canon_sha256":"014b6716c9c976cd56bc23829dabf2afcaa7cb1b95bf873a2aee26a10c21a0bc"},"schema_version":"1.0","source":{"id":"1606.04985","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1606.04985","created_at":"2026-05-18T01:12:22Z"},{"alias_kind":"arxiv_version","alias_value":"1606.04985v1","created_at":"2026-05-18T01:12:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1606.04985","created_at":"2026-05-18T01:12:22Z"},{"alias_kind":"pith_short_12","alias_value":"4PIO3SF2KJYF","created_at":"2026-05-18T12:29:58Z"},{"alias_kind":"pith_short_16","alias_value":"4PIO3SF2KJYFQSML","created_at":"2026-05-18T12:29:58Z"},{"alias_kind":"pith_short_8","alias_value":"4PIO3SF2","created_at":"2026-05-18T12:29:58Z"}],"graph_snapshots":[{"event_id":"sha256:c3167aa1f7b9c485ff76be9f1219005751278da79136c135c0b76cce81fc49b0","target":"graph","created_at":"2026-05-18T01:12:22Z","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":"Nowadays, hyperspectral image classification widely copes with spatial information to improve accuracy. One of the most popular way to integrate such information is to extract hierarchical features from a multiscale segmentation. In the classification context, the extracted features are commonly concatenated into a long vector (also called stacked vector), on which is applied a conventional vector-based machine learning technique (e.g. SVM with Gaussian kernel). In this paper, we rather propose to use a sequence structured kernel: the spectrum kernel. We show that the conventional stacked vect","authors_text":"Laetitia Chapel, S\\'ebastien Lef\\`evre, Yanwei Cui","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-06-15T21:19:54Z","title":"Combining multiscale features for classification of hyperspectral images: a sequence based kernel approach"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1606.04985","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":"sha256:5d555febb065992ca812c1d8d8195c1927063dc05a8dbb6660bcd8a6a6943cfb","target":"record","created_at":"2026-05-18T01:12:22Z","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":"2d8888581c23371e81c0eb72b0a5316237e1904019bfda6b174d0fa945090342","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-06-15T21:19:54Z","title_canon_sha256":"014b6716c9c976cd56bc23829dabf2afcaa7cb1b95bf873a2aee26a10c21a0bc"},"schema_version":"1.0","source":{"id":"1606.04985","kind":"arxiv","version":1}},"canonical_sha256":"e3d0edc8ba527058498b85b97dd5e7d08117f5ec0f8f526d73336e464925147c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e3d0edc8ba527058498b85b97dd5e7d08117f5ec0f8f526d73336e464925147c","first_computed_at":"2026-05-18T01:12:22.258900Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:12:22.258900Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"k0gd8pfu5+9SO5P9/ulwZZyHPZAbffvKHVHKQVndfMcJkxvFJfgbn8HEiLT9M4JzIfF4GgRwRlLQas0UnmKaDA==","signature_status":"signed_v1","signed_at":"2026-05-18T01:12:22.259212Z","signed_message":"canonical_sha256_bytes"},"source_id":"1606.04985","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5d555febb065992ca812c1d8d8195c1927063dc05a8dbb6660bcd8a6a6943cfb","sha256:c3167aa1f7b9c485ff76be9f1219005751278da79136c135c0b76cce81fc49b0"],"state_sha256":"c242f034c9947435f6275656d176a735a9930360d6c1517a3ba091a694ccd036"}