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.