In this paper we propose a large-scale Image annotation system for the Scalable Concept Image Annotation task. For each concept to be detected a separated classifier is built using the provided textual annotation. Images are represented as a Multivariate Gaussian distribution of a set of local features extracted over a dense regular grid. Textual analysis, on the web pages containing training images, is performed to retrieve a relevant set of samples for learning each concept classifier. An online SVMs solver based on Stochastic Gradient Descent is used to manage the large amount of training data. Experimental results show that the combination of different kind of local features encoded with our strategy achieves very competitive performance both in terms of mAP and mean F-measure.
UNIMORE at ImageCLEF 2013: Scalable concept image annotation
SERRA, Giuseppe;
2013-01-01
Abstract
In this paper we propose a large-scale Image annotation system for the Scalable Concept Image Annotation task. For each concept to be detected a separated classifier is built using the provided textual annotation. Images are represented as a Multivariate Gaussian distribution of a set of local features extracted over a dense regular grid. Textual analysis, on the web pages containing training images, is performed to retrieve a relevant set of samples for learning each concept classifier. An online SVMs solver based on Stochastic Gradient Descent is used to manage the large amount of training data. Experimental results show that the combination of different kind of local features encoded with our strategy achieves very competitive performance both in terms of mAP and mean F-measure.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
