Automatic Concept-to-Query Mapping for Web-based Concept Detector Training

Damian Borth, Adrian Ulges, Thomas Breuel
Proceedings of the International Conference on Multimedia, Scottsdale, Arizona, USA, ACM, ACM, 11/2011


Nowadays online platforms like YouTube provide massive content for training of visual concept detectors. However, it remains a difficult challenge to retrieve the right training content from such platforms. In this paper we present an approach offering an automatic concept-to-query mapping for training data acquisition. Queries are automatically constructed by a keyword term selection and a category assignment through external source like ImageNet and Google Sets. Our results demonstrate that the proposed method is able to reach retrieval results comparable to queries constructed by humans providing 76% more relevant content for detector training than an one-to-one mapping of concept names to retrieval queries would do.




@inproceedings{ BORT2011,
	Title = {Automatic Concept-to-Query Mapping for Web-based Concept Detector Training},
	Author = {Damian Borth and Adrian Ulges and Thomas Breuel},
	BookTitle = {Proceedings of the International Conference on Multimedia},
	Month = {11},
	Year = {2011},
	Publisher = {ACM},
	Organization = {ACM}

Last modified:: 30.08.2016