ML Anthology

Active Concept Learning for Image Retrieval in Dynamic Databases

Anlei Dong, Bir Bhanu
ICCV 2003 pp. 90-95
doi:10.1109/ICCV.2003.1238318 /iccv/2003/dong2003iccv-active/

Abstract

Concept learning in content-based image retrieval (CBIR) systems is a challenging task. We present an active concept learning approach based on mixture model to deal with the two basic aspects of a database system: changing (image insertion or removal) nature of a database and user queries. To achieve concept learning, we develop a novel model selection method based on Bayesian analysis that evaluates the consistency of hypothesized models with the available information. The analysis of exploitation vs. exploration in the search space helps to find optimal model efficiently. Experimental results on Corel database show the efficacy of our approach.

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Cite

Text

Dong and Bhanu. "Active Concept Learning for Image Retrieval in Dynamic Databases." IEEE/CVF International Conference on Computer Vision, 2003. doi:10.1109/ICCV.2003.1238318

Markdown

[Dong and Bhanu. "Active Concept Learning for Image Retrieval in Dynamic Databases." IEEE/CVF International Conference on Computer Vision, 2003.](https://mlanthology.org/iccv/2003/dong2003iccv-active/) doi:10.1109/ICCV.2003.1238318

BibTeX

@inproceedings{dong2003iccv-active,
  title     = {{Active Concept Learning for Image Retrieval in Dynamic Databases}},
  author    = {Dong, Anlei and Bhanu, Bir},
  booktitle = {IEEE/CVF International Conference on Computer Vision},
  year      = {2003},
  pages     = {90-95},
  doi       = {10.1109/ICCV.2003.1238318},
  url       = {https://mlanthology.org/iccv/2003/dong2003iccv-active/}
}