server time: root: http://itiis.org
current_path: /journals/tiis/digital-library/manuscript/651
current_url: http://itiis.org/journals/tiis/digital-library/manuscript/651
Topic Masks for Image Segmentation
  • KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Topic Masks for Image Segmentation

Vol. 7, No. 12, December 28, 2013
10.3837/tiis.2013.12.018, Download Paper (Free):


Unsupervised methods for image segmentation are recently drawing attention because most images do not have labels or tags. A topic model is such an unsupervised probabilistic method that captures latent aspects of data, where each latent aspect, or a topic, is associated with one homogeneous region. The results of topic models, however, usually have noises, which decreases the overall segmentation performance. In this paper, to improve the performance of image segmentation using topic models, we propose two topic masks applicable to topic assignments of homogeneous regions obtained from topic models. The topic masks capture the noises among the assigned topic assignments or topic labels, and remove the noises by replacements, just like image masks for pixels. However, as the nature of topic assignments is different from image pixels, the topic masks have properties that are different from the existing image masks for pixels. There are two contributions of this paper. First, the topic masks can be used to reduce the noises of topic assignments obtained from topic models for image segmentation tasks. Second, we test the effectiveness of the topic masks by applying them to segmented images obtained from the Latent Dirichlet Allocation model and the Spatial Latent Dirichlet Allocation model upon the MSRC image dataset. The empirical results show that one of the masks successfully reduces the topic noises.


Show / Hide Statistics

Statistics (Cumulative Counts from December 1st, 2015)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.

Cite this article

[IEEE Style]
Y. Jeong, C. Lim, B. Jeong and H. Choi, "Topic Masks for Image Segmentation," KSII Transactions on Internet and Information Systems, vol. 7, no. 12, pp. 3274-3292, 2013. DOI: 10.3837/tiis.2013.12.018.

[ACM Style]
Young-Seob Jeong, Chae-Gyun Lim, Byeong-Soo Jeong, and Ho-Jin Choi. 2013. Topic Masks for Image Segmentation. KSII Transactions on Internet and Information Systems, 7, 12, (2013), 3274-3292. DOI: 10.3837/tiis.2013.12.018.