On the creation of visual models for keywords through crowdsourcing
Date Issued
2012
Author(s)
Abstract
Crowdsourcing annotation is a recent development since a complete and elaborate annotation of the
content of an image is an extremely labour-intensive and time consuming task. In this paper we examine the
possibility to build accurate visual models for keywords created through crowdsourcing. Specifically, 8 different
keywords related to athletics domain have been modelled using MPEG-7 and Histogram of Oriented Gradients
(HOG) low level features and the Sequential Minimal Optimization (SMO) classifier. The experimental results
have been examined using accuracy metrics and are very promising showing the ability of the visual models to
classify the images into the 8 classes with the highest average accuracy rate of 73.13% in the purpose of the HOG
features.
content of an image is an extremely labour-intensive and time consuming task. In this paper we examine the
possibility to build accurate visual models for keywords created through crowdsourcing. Specifically, 8 different
keywords related to athletics domain have been modelled using MPEG-7 and Histogram of Oriented Gradients
(HOG) low level features and the Sequential Minimal Optimization (SMO) classifier. The experimental results
have been examined using accuracy metrics and are very promising showing the ability of the visual models to
classify the images into the 8 classes with the highest average accuracy rate of 73.13% in the purpose of the HOG
features.
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