Open AccessSemi‐automatic object segmentation using colour invariance and Graph Cuts
Author(s) -
Yuan Xingsheng,
Xiang Fengtao,
Wang Zhengzhi
Publication year - 2014
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2013.0154
Subject(s) - artificial intelligence , segmentation , cut , computer vision , pattern recognition (psychology) , graph , invariant (physics) , image segmentation , pixel , computer science , scale space segmentation , segmentation based object categorization , object (grammar) , feature (linguistics) , mathematics , linguistics , philosophy , mathematical physics , theoretical computer science
Conventional semi‐automatic or interactive methods, which require a small amount of user inputs for region segmentation of objects, have obtained the best segmentation results. A new semi‐automatic segmentation technique by using coloured scale‐invariant feature transform (CSIFT) to extract seed pixels in Graph Cuts is introduced here. First, CSIFT is used to extract feature points of objects in the image. Then, a voting process is used to extract the matched points as object seeds. The detailed technique via s–t Graph Cuts has been presented, and a new segmentation energy cost function with two colour‐invariant descriptors has been proposed: colour‐name descriptor and colour‐shade descriptor. The colour‐name descriptor introduces high‐level considerations resembling top‐down intervention, and the colour‐shade descriptor allows us to include physical consideration derived from the image formation model capturing gradual colour surface variations and provides congruencies in the presence of shadows and highlights in the segmentation. The experimental results prove that the proposed method provides high‐quality segmentations with object details.