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Suggesting an Integration System for Image Annotation

  • 1207: Innovations in Multimedia Information Processing & Retrieval​
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Abstract

The number of digital images uploaded in the virtual world is rapidly growing every day. Therefore, an automatic image annotation system that can retrieve information from these images seems to be in high demand. One of the challenges in this field is the imbalanced data sets and the difficulty of successfully learning tags from them. Even if a nearly balanced data set exists for image annotation, it is unlikely to find a single learner, which could learn all tags with the same accuracy. In this paper, we suggest a novel integration system that selects an elite group of models from all existing annotation models and then combines them to take the best advantage of each model’s learning technique. The proposed system studies the data sets of selected models without the need for direct access to those data sets. As this algorithm is independent of the annotation models or data sets, it could be used to combine the currently available annotation models and those developed in future, along with their data sets and learning models. We believe the proposed approach has the potential of becoming an integrated ground for automatic image annotation models.

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  1. Department of Computer Engineering, Sharif University of Technology, 11365-11155, Tehran, Iran

    Artin Ghostan Khatchatoorian & Mansour Jamzad

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  1. Artin Ghostan Khatchatoorian
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Ghostan Khatchatoorian, A., Jamzad, M. Suggesting an Integration System for Image Annotation. Multimed Tools Appl 82, 8323–8343 (2023). https://doi.org/10.1007/s11042-021-11571-y

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