Scalable Visual Instance Mining with Instance Graph

Wei Li, Changhu Wang, Lei Zhang, Yong Rui and Bo Zhang

Abstract

In this paper we address the problem of visual instance mining, which is to automatically discover frequently appearing visual instances from a large collection of images. We propose a scalable mining method by leveraging the graph structure with images as vertices. Different from most existing work that focused on either instance-level similarities or image-level context properties, our graph captures both information. The instance-level information is integrated during the construction of a weighted and undirected instance graph based on the similarity between augmented local features, while the image-level context is explored with a greedy breadth-first search algorithm to discover clusters of visual instances from the graph. This method is capable of mining challenging small visual instances with diverse variations. We evaluated our method on two fully annotated datasets and outperformed the state of the arts on both datasets with higher recalls. We also applied our method on a one-million Flickr dataset and proved its scalability.

Session

Learning and Recognition

Files

Extended Abstract (PDF, 361K)
Paper (PDF, 1940K)

DOI

10.5244/C.29.98
https://dx.doi.org/10.5244/C.29.98

Citation

Bibtex

@inproceedings{BMVC2015_98,
	title={Scalable Visual Instance Mining with Instance Graph},
	author={Wei Li and Changhu Wang and Lei Zhang and Yong Rui and Bo Zhang},
	year={2015},
	month={September},
	pages={98.1-98.11},
	articleno={98},
	numpages={11},
	booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
	publisher={BMVA Press},
	editor={Xianghua Xie, Mark W. Jones, and Gary K. L. Tam},
	doi={10.5244/C.29.98},
	isbn={1-901725-53-7},
	url={https://dx.doi.org/10.5244/C.29.98}
}