A Direct Method for Semantic Partitioning of Low-level Image Data
- DOI
- 10.2991/eeic-13.2013.25How to use a DOI?
- Keywords
- Single Concept Clustering(SCC); semantic parti-tioning; data processing tasks; Rough Set
- Abstract
It is a tough task to discover semantics implied by low-level image data. In view of this situation, single concept clustering (SCC), a new algorithm for semantic partitioning of data set according to a single concept is presented. First, data is preprocessed and an uniform interface obtained for the follow-up processing. Secondly, data set is described by one Gaussian and all cases in which classes meet element gain or loss are classified into eight kinds. Three new theorems and two lemmas are established from the analyses of the eight cases. According to these theorems and lemmas, we combine the eight cases with the situations in which a class doesn’t meet any element gain and loss, remove the relations between the previous class and the current class, form the relations between the current class and the succeeding class, and then draw four combinations. Finnally, data set is adaptively decomposed into semantic partitions with the four combinations. The experiments using the data of color images as the test data demonstrates that the SCC method can find sparse connected regions implying semantics, which lays a foundation for image label and analysis. Furthermore, the SCC method may also be used in other data processing tasks, for an example, determining equivalence classes of rough set.
- Copyright
- © 2013, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Zhongsheng Li AU - Tongcheng Huang PY - 2013/12 DA - 2013/12 TI - A Direct Method for Semantic Partitioning of Low-level Image Data BT - Proceedings of the 3rd International Conference on Electric and Electronics PB - Atlantis Press SP - 108 EP - 111 SN - 1951-6851 UR - https://doi.org/10.2991/eeic-13.2013.25 DO - 10.2991/eeic-13.2013.25 ID - Li2013/12 ER -