%0 Journal Article
%T Visual novelty driven incremental and autonomous visual learning algorithm<br>视觉陌生度驱动的增量自主式视觉学习算法
%A Qu Xinyu
%A Yao Minghai
%A Gu Qinlong
%A <br>瞿心昱
%A 姚明海
%A 顾勤龙
%J 中国图象图形学报
%D 2012
%I 
%X In intelligent robot design,the traditional machine learning paradigm is commonly used.However,the traditional methods cause problems in visual tasks such as low learning initiative,lack of adaptability with uncertainty and bad expansibility of knowledge and ability.According to the new research direction called cognitive development learning,a visual novelty driven incremental and autonomous visual learning algorithm is proposed,in which the internal motivation is defined as visual novelty which is calculated by online PCA.The autonomous learning and accumulation of knowledge is implemented in the form of updating PCA subspace,which is guided by internally motivated Q-learning using visual novelty.Equipped with the proposed algorithm,a robot makes the next learning decision by judging the novelty between learned knowledge and what is seen now.Experimental results show that the algorithm has the ability of autonomous exploring and learning,actively guiding the robot to learn new knowledge,acquire knowledge and develop intelligence online and in incremental manner.
%K cognitive development
%K internal motivation
%K visual novelty
%K online principal component analysis
%K Q-learning<br>认知发育
%K 内部动机
%K 视觉陌生度
%K 在线主成分分析
%K Q学习
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=D06194629680C940ACE75262F54B9D85&aid=C4354F1915DDC640F7C33C399783BBB1&yid=99E9153A83D4CB11&vid=BCA2697F357F2001&iid=B31275AF3241DB2D&sid=DA280A426E11FC95&eid=710C005323C0774A&journal_id=1006-8961&journal_name=中国图象图形学报&referenced_num=0&reference_num=25