Mixing Low-Level and Semantic Features for Image Interpretation - A Framework and a Simple Case Study
Abstract
Semantic Content-Based Image Retrieval (SCBIR) allows users to retrieve images via complex expressions of some ontological language describing a domain of interest. SCBIR adds some flexibility to the state-of-the-art methods for image retrieval, which support query either by keywords or by image examples. The price for this additional flexibility is the generation of a semantically rich description of the image content reflecting the ontology constraints. Generating these semantic interpretations is an open research problem. This paper contributes to this research line by proposing an approach for SCBIR based on the somehow natural idea that the interpretation of a picture is an (onto) logical model of an ontology that describes the domain of the picture. We implement this idea in an unsupervised method that jointly exploits the ontological constraints and the low-level features of the image. The preliminary evaluation, presented in the paper, shows promising results.
Cite
Text
Donadello and Serafini. "Mixing Low-Level and Semantic Features for Image Interpretation - A Framework and a Simple Case Study." European Conference on Computer Vision Workshops, 2014. doi:10.1007/978-3-319-16181-5_20Markdown
[Donadello and Serafini. "Mixing Low-Level and Semantic Features for Image Interpretation - A Framework and a Simple Case Study." European Conference on Computer Vision Workshops, 2014.](https://mlanthology.org/eccvw/2014/donadello2014eccvw-mixing/) doi:10.1007/978-3-319-16181-5_20BibTeX
@inproceedings{donadello2014eccvw-mixing,
title = {{Mixing Low-Level and Semantic Features for Image Interpretation - A Framework and a Simple Case Study}},
author = {Donadello, Ivan and Serafini, Luciano},
booktitle = {European Conference on Computer Vision Workshops},
year = {2014},
pages = {283-298},
doi = {10.1007/978-3-319-16181-5_20},
url = {https://mlanthology.org/eccvw/2014/donadello2014eccvw-mixing/}
}