Abstract
Semi-supervised Method of Multiple Object Segmentation with a Region Labeling and Flood Fill
Highlights
IntroductionCLASS-SPECIFIC (or category-level) multiple object segmentation is one of the fundamental problems in computer vision and object recognition
CLASS-SPECIFIC multiple object segmentation is one of the fundamental problems in computer vision and object recognition
To see how Similar Region Merging Flood Fill produces promising segmentation results in the case that there is a large variation in shape within an object class
Summary
CLASS-SPECIFIC (or category-level) multiple object segmentation is one of the fundamental problems in computer vision and object recognition. There has been a substantial amount of research on image segmentation including clustering based methods, region growing methods [5], histogram based methods [6], and more recent one such as adaptive thresh-hold methods [7], level set methods [8], graph based methods [4, 9] etc. As the variance of object color/texture, shape within an object class can be large, it is to difficult to obtain class-specific features that can describe object class accurately. In this regards, multiple object segmentation is a difficult problem. Multiple object segmentation is feasible due to the recent development of recognition and over segmentation (we shall use this in place of image segmentation) technique in computer vision
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