{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:GG3MBZLQEWBEXISWLWV7EAXSSS","short_pith_number":"pith:GG3MBZLQ","schema_version":"1.0","canonical_sha256":"31b6c0e57025824ba2565dabf202f294912581d9309038c621aa867af83cbe33","source":{"kind":"arxiv","id":"1610.01906","version":4},"attestation_state":"computed","paper":{"title":"Utilizing High-level Visual Feature for Indoor Shopping Mall Navigation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guyue Zhou, Haitian Zheng, Lu Fang, Minjian Pang, Xiongfei Su, Yangchun Zhu, Ziwei Xu","submitted_at":"2016-10-06T15:14:47Z","abstract_excerpt":"Towards robust and convenient indoor shopping mall navigation, we propose a novel learning-based scheme to utilize the high-level visual information from the storefront images captured by personal devices of users. Specifically, we decompose the visual navigation problem into localization and map generation respectively. Given a storefront input image, a novel feature fusion scheme (denoted as FusionNet) is proposed by fusing the distinguishing DNN-based appearance feature and text feature for robust recognition of store brands, which serves for accurate localization. Regarding the map generat"},"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":"1610.01906","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-10-06T15:14:47Z","cross_cats_sorted":[],"title_canon_sha256":"1f10a95303b75199aad69235f6bbe72c7087bb95726784a31480c82b66d531ae","abstract_canon_sha256":"4e67325313b866f5a92348d1ba776b898641614f908762df5696c857c9023702"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:50:30.875986Z","signature_b64":"lgclNix0zIdVqgd4ZfrCliJ7uF3HEwi4hnsrXoc9VY5aKrq6IRysgw038jf3lewFmlry1JFNrFeSFdtceoicBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"31b6c0e57025824ba2565dabf202f294912581d9309038c621aa867af83cbe33","last_reissued_at":"2026-05-18T00:50:30.875158Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:50:30.875158Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Utilizing High-level Visual Feature for Indoor Shopping Mall Navigation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guyue Zhou, Haitian Zheng, Lu Fang, Minjian Pang, Xiongfei Su, Yangchun Zhu, Ziwei Xu","submitted_at":"2016-10-06T15:14:47Z","abstract_excerpt":"Towards robust and convenient indoor shopping mall navigation, we propose a novel learning-based scheme to utilize the high-level visual information from the storefront images captured by personal devices of users. Specifically, we decompose the visual navigation problem into localization and map generation respectively. Given a storefront input image, a novel feature fusion scheme (denoted as FusionNet) is proposed by fusing the distinguishing DNN-based appearance feature and text feature for robust recognition of store brands, which serves for accurate localization. Regarding the map generat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1610.01906","kind":"arxiv","version":4},"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":"1610.01906","created_at":"2026-05-18T00:50:30.875278+00:00"},{"alias_kind":"arxiv_version","alias_value":"1610.01906v4","created_at":"2026-05-18T00:50:30.875278+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1610.01906","created_at":"2026-05-18T00:50:30.875278+00:00"},{"alias_kind":"pith_short_12","alias_value":"GG3MBZLQEWBE","created_at":"2026-05-18T12:30:19.053100+00:00"},{"alias_kind":"pith_short_16","alias_value":"GG3MBZLQEWBEXISW","created_at":"2026-05-18T12:30:19.053100+00:00"},{"alias_kind":"pith_short_8","alias_value":"GG3MBZLQ","created_at":"2026-05-18T12:30:19.053100+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/GG3MBZLQEWBEXISWLWV7EAXSSS","json":"https://pith.science/pith/GG3MBZLQEWBEXISWLWV7EAXSSS.json","graph_json":"https://pith.science/api/pith-number/GG3MBZLQEWBEXISWLWV7EAXSSS/graph.json","events_json":"https://pith.science/api/pith-number/GG3MBZLQEWBEXISWLWV7EAXSSS/events.json","paper":"https://pith.science/paper/GG3MBZLQ"},"agent_actions":{"view_html":"https://pith.science/pith/GG3MBZLQEWBEXISWLWV7EAXSSS","download_json":"https://pith.science/pith/GG3MBZLQEWBEXISWLWV7EAXSSS.json","view_paper":"https://pith.science/paper/GG3MBZLQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1610.01906&json=true","fetch_graph":"https://pith.science/api/pith-number/GG3MBZLQEWBEXISWLWV7EAXSSS/graph.json","fetch_events":"https://pith.science/api/pith-number/GG3MBZLQEWBEXISWLWV7EAXSSS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GG3MBZLQEWBEXISWLWV7EAXSSS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GG3MBZLQEWBEXISWLWV7EAXSSS/action/storage_attestation","attest_author":"https://pith.science/pith/GG3MBZLQEWBEXISWLWV7EAXSSS/action/author_attestation","sign_citation":"https://pith.science/pith/GG3MBZLQEWBEXISWLWV7EAXSSS/action/citation_signature","submit_replication":"https://pith.science/pith/GG3MBZLQEWBEXISWLWV7EAXSSS/action/replication_record"}},"created_at":"2026-05-18T00:50:30.875278+00:00","updated_at":"2026-05-18T00:50:30.875278+00:00"}