TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Topic Masks for Image Segmentation |
| 영문제목(English Title) |
Topic Masks for Image Segmentation |
| 저자(Author) |
Young-Seob Jeong
Chae-Gyun Lim
Byeong-Soo Jeong
Ho-Jin Choi
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| 원문수록처(Citation) |
VOL 07 NO. 12 PP. 3274 ~ 3292 (2013. 12) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
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.
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| 키워드(Keyword) |
Topic mining
image segmentation
topic mask
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| 파일첨부 |
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