MULTI-CLASS REGION MERGING FOR INTERACTIVE IMAGE SEGMENTATION USING HIERARCHICAL CLUSTERING ANALYSIS

In interactive image segmentation, distance calculation between regions and sequence of region merging is being an important thing that needs to be considered to obtain accurate segmentation results. Region merging without regard to label in Hierarchical Clustering Analysis causes the possibility of two different labels merged into a cluster and resulting errors in segmentation. This study proposes a new multi-class region merging strategy for interactive image segmentation using the Hierarchical Clustering Analysis. Marking is given to regions that are considered as objects and background, which are then referred as classes. A different label for each class is given to prevent any classes with different label merged into a cluster. Based on experiment, the mean value of ME and RAE for the results of segmentation using the proposed method are 0.035 and 0.083, respectively. Experimental results show that giving the label on each class is effectively used in multi-class region merging.

• Publication year

  2019

• Venue

  Jurnal Ilmu Komputer dan Informasi

• Publication date

  2019-07-08

• Fields of study

  Computer Science

• Identifiers
  DOI 10.21609/JIKI.V12I2.757
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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