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Communication Dans Un Congrès Année : 2022

Multi-instance Learning for Semantic Image Analysis

Dongping Tian
  • Fonction : Auteur
  • PersonId : 1275161
Ying Zhang
  • Fonction : Auteur
  • PersonId : 1275162

Résumé

Semantic image analysis is an active topic of research in computer vision and pattern recognition. In the last two decades, a large number of works on semantic image analysis have emerged, among which the multi-instance learning (MIL) is one of the most commonly used methods due to its theoretical interest and its applicability to real-world problems. However, compared with various MIL methods and their corresponding applications in the field of semantic image analysis, there is a lack of surveys or review researches about MIL related studies. So the current paper, to begin with, elaborates the basic principles of multi-instance learning, subsequently summarizes it with applications to semantic based- image annotation, image retrieval and image classification as well as several other related applications comprehensively. At length, this paper concludes with a summary of some important conclusions and several potential research directions of MIL in the area of semantic image analysis for the future.
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Dates et versions

hal-04178732 , version 1 (08-08-2023)

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Dongping Tian, Ying Zhang. Multi-instance Learning for Semantic Image Analysis. 12th International Conference on Intelligent Information Processing (IIP), May 2022, Qingdao, China. pp.473-484, ⟨10.1007/978-3-031-03948-5_38⟩. ⟨hal-04178732⟩
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