Open AccessObject segmentation framework based on dictionary‐group and sparse shape representation
Author(s) -
Yao Jincao,
Yu Huimin,
Hu Roland
Publication year - 2017
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2016.3744
Subject(s) - segmentation , artificial intelligence , pattern recognition (psychology) , representation (politics) , object (grammar) , sparse approximation , computer science , image segmentation , scale space segmentation , set (abstract data type) , decomposition , segmentation based object categorization , group (periodic table) , function (biology) , computer vision , ecology , chemistry , organic chemistry , evolutionary biology , politics , political science , law , biology , programming language
Given an input object whose shape is partly similar to some of the samples in the training set, a dictionary‐group‐based sparse model is introduced that can use the local information of those less similar shape neighbours to represent the object and guide the segmentation. The model follows from a new sparse energy function that combines a series of sparse local constraints with the fuzzy log‐polar decomposition‐based shape elements. Finally, a unified framework is built to connect the high‐level shape representation with the low‐level image segmentation. The model on the public datasets is tested, and the experimental results show the superior shape segmentation capabilities of the proposed model.