Paper Info
Title
Robot users for the evaluation of boundary-tracking approaches in interactive image segmentation
Abstract
Recent advances in interactive image segmentation focused on eliminating the user bias during evaluation by simulating their behavior using robot users. However, these robots only work for region-based methods, excluding the important class of approaches that rely on the boundary-tracking paradigm. We propose completely novel robot users that are able to simulate the human user behavior when segmenting an image through the addition of anchor points close to the object's boundary. Our robots constantly evaluate the optimum-boundary segment being computed from a previously selected anchor point to the current virtual mouse position, seeking for the longest possible segment with minimum acceptable error. A new anchor is added when the error is too high and the robots iterate until closing the contour, just like real users. We validate our robots by conducting a user study and extensive experiments, considering two boundary-tracking methods: live-wire-on-the-fly and Riverbed. We further show how our robot users can be used to assess hybrid approaches that combine boundary-tracking with region-based delineation, such as LiveMarkers, while conjecturing that robots might lead to new methods for automatic foreground segmentation.
Year
DOI
Venue
2014
10.1109/ICIP.2014.7025657
Image Processing
Keywords
Field
DocType
image segmentation,mouse controllers (computers),object tracking,robots,LiveMarkers,Riverbed,anchor point,automatic foreground segmentation,boundary-tracking approach evaluation,human user behavior simulation,interactive image segmentation,live-wire-on-the-fly,optimum-boundary segment,region-based delineation,region-based methods,robot users,user bias elimination,virtual mouse position,Boundary-tracking,Image Foresting Transform,Interactive image segmentation,Live wire,Riverbed,Robot users
Computer vision,Market segmentation,Scale-space segmentation,Virtual mouse,Pattern recognition,Computer science,Segmentation,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Robot
Conference
ISSN
Citations 
PageRank 
1522-4880
2
0.37
References 
Authors
18
3
Name
Order
Citations
PageRank
Thiago Vallin Spina1457.32
Alexandre X. Falcão21877132.30
Falcao, A.X.338622.31