{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T08:17:28Z","timestamp":1782375448481,"version":"3.54.5"},"reference-count":58,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2021,7,22]],"date-time":"2021-07-22T00:00:00Z","timestamp":1626912000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2021,8,31]]},"abstract":"<jats:p>\n            Lifelog analytics is an emerging research area with technologies embracing the latest advances in machine learning, wearable computing, and data analytics. However, state-of-the-art technologies are still inadequate to distill voluminous multimodal lifelog data into high quality insights. In this article, we propose a novel\n            <jats:bold>semantic relevance mapping<\/jats:bold>\n            (\n            <jats:bold>SRM<\/jats:bold>\n            ) method to tackle the problem of lifelog information access. We formulate lifelog image retrieval as a series of mapping processes where a semantic gap exists for relating basic semantic attributes with high-level query topics. The SRM serves both as a formalism to construct a trainable model to bridge the semantic gap and an algorithm to implement the training process on real-world lifelog data. Based on the SRM, we propose a computational framework of lifelog analytics to support various applications of lifelog information access, such as image retrieval, summarization, and insight visualization. Systematic evaluations are performed on three challenging benchmarking tasks to show the effectiveness of our method.\n          <\/jats:p>","DOI":"10.1145\/3446209","type":"journal-article","created":{"date-parts":[[2021,7,22]],"date-time":"2021-07-22T14:44:29Z","timestamp":1626965069000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Lifelog Image Retrieval Based on Semantic Relevance Mapping"],"prefix":"10.1145","volume":"17","author":[{"given":"Qianli","family":"Xu","sequence":"first","affiliation":[{"name":"Institute for Infocomm Research, A*STAR, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ana Garcia Del","family":"Molino","sequence":"additional","affiliation":[{"name":"Institute for Infocomm Research, A*STAR, Singapore; ByteDance AI Lab, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Lin","sequence":"additional","affiliation":[{"name":"Institute for Infocomm Research, A*STAR, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fen","family":"Fang","sequence":"additional","affiliation":[{"name":"Institute for Infocomm Research, A*STAR, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vigneshwaran","family":"Subbaraju","sequence":"additional","affiliation":[{"name":"A*STAR Human-Centric Artificial Intelligence Programme;Institute of High Performance Computing, A*STAR, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liyuan","family":"Li","sequence":"additional","affiliation":[{"name":"Institute for Infocomm Research, A*STAR, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joo-Hwee","family":"Lim","sequence":"additional","affiliation":[{"name":"Institute for Infocomm Research, A*STAR, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,7,22]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"crossref","unstructured":"F. 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