Image Hub Explorer: Evaluating Representations and Metrics for Content-Based Image Retrieval and Object Recognition
Abstract
Large quantities of image data are generated daily and visualizing large image datasets is an important task. We present a novel tool for image data visualization and analysis, Image Hub Explorer. The integrated analytic functionality is centered around dealing with the recently described phenomenon of hubness and evaluating its impact on the image retrieval, recognition and recommendation process. Hubness is reflected in that some images ( hubs ) end up being very frequently retrieved in ’top k ’ result sets, regardless of their labels and target semantics. Image Hub Explorer offers many methods that help in visualizing the influence of major image hubs, as well as state-of-the-art metric learning and hubness-aware classification methods that help in reducing the overall impact of extremely frequent neighbor points. The system also helps in visualizing both beneficial and detrimental visual words in individual images. Search functionality is supported, along with the recently developed hubness-aware result set re-ranking procedure.
Cite
Text
Tomasev and Mladenic. "Image Hub Explorer: Evaluating Representations and Metrics for Content-Based Image Retrieval and Object Recognition." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2013. doi:10.1007/978-3-642-40994-3_44Markdown
[Tomasev and Mladenic. "Image Hub Explorer: Evaluating Representations and Metrics for Content-Based Image Retrieval and Object Recognition." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2013.](https://mlanthology.org/ecmlpkdd/2013/tomasev2013ecmlpkdd-image/) doi:10.1007/978-3-642-40994-3_44BibTeX
@inproceedings{tomasev2013ecmlpkdd-image,
title = {{Image Hub Explorer: Evaluating Representations and Metrics for Content-Based Image Retrieval and Object Recognition}},
author = {Tomasev, Nenad and Mladenic, Dunja},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2013},
pages = {637-640},
doi = {10.1007/978-3-642-40994-3_44},
url = {https://mlanthology.org/ecmlpkdd/2013/tomasev2013ecmlpkdd-image/}
}