Year: 2015
Type: conference-paper
Abstract: This paper presents the results of an experimental study of the popular Bag-of-Features (BoF) model for the application of unsupervised learning of images, or image clustering.Although this method has been extensively applied for image classification and scene recognition, there has been few works which employ it in an unsupervised way.Also, due to the fact that the BoF model requires a great amount of steps, algorithms and parameter settings, we felt like there was a lack of detailed studies about the subject.We implemented testing routines in Python which we made publicly available in GitHub.In order to assess the performance of the model, three image datasets were used, namely, Coil-20 dataset, Natural and Urban dataset and Event dataset.The results obtained indicate that the BoF method provides a good representation of simple image collections for the purpose of clustering.However, it requires fine tunning of the parameters and algorithms for each dataset and obtains poor results for more complex scene datasets.We can therefore conclude that more advanced techniques are required in order to be able to effectively extract information from large image collections.
DOI: 10.5244/c.29.146
Authors: Mariana Afonso, Luis F. Teixeira
Institutions: Universidade do Porto, INESC TEC
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2317232248