{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:4NCO6OATLM45TLG46ZLKBG7XCT","short_pith_number":"pith:4NCO6OAT","schema_version":"1.0","canonical_sha256":"e344ef38135b39d9acdcf656a09bf714d970697f0d7bf04c798e582be76a8132","source":{"kind":"arxiv","id":"1906.04987","version":3},"attestation_state":"computed","paper":{"title":"Indoor image representation by high-level semantic features","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chiranjibi Sitaula, Sunil Aryal, Xuequan Lu, Yong Xiang, Yushu Zhang","submitted_at":"2019-06-12T07:53:26Z","abstract_excerpt":"Indoor image features extraction is a fundamental problem in multiple fields such as image processing, pattern recognition, robotics and so on. Nevertheless, most of the existing feature extraction methods, which extract features based on pixels, color, shape/object parts or objects on images, suffer from limited capabilities in describing semantic information (e.g., object association). These techniques, therefore, involve undesired classification performance. To tackle this issue, we propose the notion of high-level semantic features and design four steps to extract them. Specifically, we fi"},"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":"1906.04987","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-12T07:53:26Z","cross_cats_sorted":[],"title_canon_sha256":"df989786c1f7e4cb9a369dd92a59fe2e5fbc157b3f42baaef1ed6b57a943dd25","abstract_canon_sha256":"e3642748c85cd0b8ec898563571f7b9e873bca5db7da4545ccee277ffba0f2ce"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:35:00.914278Z","signature_b64":"grIdm8ORuDKPnosqJc3yrYVub95DPIeqjciYA2Lt6lBdRQsaMC7/TrYqBPsgeCnsB7wLdstzqPsBvRuAK3/IAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e344ef38135b39d9acdcf656a09bf714d970697f0d7bf04c798e582be76a8132","last_reissued_at":"2026-07-05T00:35:00.913816Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:35:00.913816Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Indoor image representation by high-level semantic features","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chiranjibi Sitaula, Sunil Aryal, Xuequan Lu, Yong Xiang, Yushu Zhang","submitted_at":"2019-06-12T07:53:26Z","abstract_excerpt":"Indoor image features extraction is a fundamental problem in multiple fields such as image processing, pattern recognition, robotics and so on. Nevertheless, most of the existing feature extraction methods, which extract features based on pixels, color, shape/object parts or objects on images, suffer from limited capabilities in describing semantic information (e.g., object association). These techniques, therefore, involve undesired classification performance. To tackle this issue, we propose the notion of high-level semantic features and design four steps to extract them. Specifically, we fi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.04987","kind":"arxiv","version":3},"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/1906.04987/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":"1906.04987","created_at":"2026-07-05T00:35:00.913871+00:00"},{"alias_kind":"arxiv_version","alias_value":"1906.04987v3","created_at":"2026-07-05T00:35:00.913871+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.04987","created_at":"2026-07-05T00:35:00.913871+00:00"},{"alias_kind":"pith_short_12","alias_value":"4NCO6OATLM45","created_at":"2026-07-05T00:35:00.913871+00:00"},{"alias_kind":"pith_short_16","alias_value":"4NCO6OATLM45TLG4","created_at":"2026-07-05T00:35:00.913871+00:00"},{"alias_kind":"pith_short_8","alias_value":"4NCO6OAT","created_at":"2026-07-05T00:35:00.913871+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/4NCO6OATLM45TLG46ZLKBG7XCT","json":"https://pith.science/pith/4NCO6OATLM45TLG46ZLKBG7XCT.json","graph_json":"https://pith.science/api/pith-number/4NCO6OATLM45TLG46ZLKBG7XCT/graph.json","events_json":"https://pith.science/api/pith-number/4NCO6OATLM45TLG46ZLKBG7XCT/events.json","paper":"https://pith.science/paper/4NCO6OAT"},"agent_actions":{"view_html":"https://pith.science/pith/4NCO6OATLM45TLG46ZLKBG7XCT","download_json":"https://pith.science/pith/4NCO6OATLM45TLG46ZLKBG7XCT.json","view_paper":"https://pith.science/paper/4NCO6OAT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1906.04987&json=true","fetch_graph":"https://pith.science/api/pith-number/4NCO6OATLM45TLG46ZLKBG7XCT/graph.json","fetch_events":"https://pith.science/api/pith-number/4NCO6OATLM45TLG46ZLKBG7XCT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4NCO6OATLM45TLG46ZLKBG7XCT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4NCO6OATLM45TLG46ZLKBG7XCT/action/storage_attestation","attest_author":"https://pith.science/pith/4NCO6OATLM45TLG46ZLKBG7XCT/action/author_attestation","sign_citation":"https://pith.science/pith/4NCO6OATLM45TLG46ZLKBG7XCT/action/citation_signature","submit_replication":"https://pith.science/pith/4NCO6OATLM45TLG46ZLKBG7XCT/action/replication_record"}},"created_at":"2026-07-05T00:35:00.913871+00:00","updated_at":"2026-07-05T00:35:00.913871+00:00"}