Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/7009
Title: Modelling crowdsourcing originated keywords within the athletics domain
Authors: Tsapatsoulis, Nicolas 
Theodosiou, Zenonas 
Keywords: Artificial intelligence
Information technology
Athletics
Issue Date: 2012
Publisher: Springer
Source: 8th IFIP WG 12.5 international conference, AIAI 2012, Halkidiki, Greece, September 27-30
Abstract: Image classification arises as an important phase in the overall process of automatic image annotation and image retrieval. Usually, a set of manually annotated images is used to train supervised systems and classify images into classes. The act of crowdsourcing has largely focused on investigating strategies for reducing the time, cost and effort required for the creation of the annotated data. In this paper we experiment with the efficiency of various classifiers in building visual models for keywords through crowdsourcing with the aid of Weka tool and a variety of low-level features. A total number of 500 manually annotated images related to athletics domain are used to build and test 8 visual models. The experimental results have been examined using the classification accuracy and are very promising showing the ability of the visual models to classify the images into the corresponding classes with the highest average classification accuracy of 74.38% in the purpose of SMO data classifier
URI: http://ktisis.cut.ac.cy/handle/10488/7009
DOI: 10.1007/978-3-642-33409-2_42
Rights: © IFIP International Federation for Information Processing
Appears in Collections:Δημοσιεύσεις σε συνέδρια/Conference papers

Show full item record

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.