Lifelog Image Retrieval Based on Semantic Relevance Mapping

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Lifelog Image Retrieval Based on Semantic Relevance Mapping
Title:
Lifelog Image Retrieval Based on Semantic Relevance Mapping
Other Titles:
ACM Transactions on Multimedia Computing, Communications, and Applications
Publication Date:
22 July 2021
Citation:
Xu, Q., Molino, A. G. D., Lin, J., Fang, F., Subbaraju, V., Li, L., & Lim, J.-H. (2021). Lifelog Image Retrieval Based on Semantic Relevance Mapping. ACM Transactions on Multimedia Computing, Communications, and Applications, 17(3), 1–18. doi:10.1145/3446209
Abstract:
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 semantic relevance mapping (SRM) 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.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - JCO REVIVE Project
Grant Reference no. : 1335h0009
Description:
ISSN:
1551-6857
1551-6865
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