Clustering Image Search Results by Entity Disambiguation
Abstract
Existing key-word based image search engines return images whose title or immediate surrounding text contains the search term as a keyword. When the search term is ambiguous and means different things, the results often come in a mixed bag of different entities. This paper proposes a novel framework that understands the context and thus infers the most likely entity in the given image by disambiguating the terms in the context into the corresponding concepts from external knowledge in a process called conceptualization. The images can subsequently be clustered by the most likely associated entities. This approach outperforms the best competing image clustering techniques by 29.2% in NMI score. In addition, the framework automatically annotates each cluster of images by its key entities which allows users to quickly identify the images they want.
Cite
Text
Zhao et al. "Clustering Image Search Results by Entity Disambiguation." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2014. doi:10.1007/978-3-662-44845-8_24Markdown
[Zhao et al. "Clustering Image Search Results by Entity Disambiguation." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2014.](https://mlanthology.org/ecmlpkdd/2014/zhao2014ecmlpkdd-clustering/) doi:10.1007/978-3-662-44845-8_24BibTeX
@inproceedings{zhao2014ecmlpkdd-clustering,
title = {{Clustering Image Search Results by Entity Disambiguation}},
author = {Zhao, Kaiqi and Cai, Zhiyuan and Sui, Qingyu and Wei, Enxun and Zhu, Kenny Q.},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2014},
pages = {369-384},
doi = {10.1007/978-3-662-44845-8_24},
url = {https://mlanthology.org/ecmlpkdd/2014/zhao2014ecmlpkdd-clustering/}
}