Unstructured robot perception through Internet semantic concept learning
Fengchao Wang, Dongdong Chen, Peijiang Yuan · 2014
Intelligent robot is one of the most important ongoing technologies both in industry and social life. Smart perception is the key technology for intelligent robots. Lack of training data, there has been many barriers for intelligent robot to learn the unstructured environment. In this paper, an automatic data mining method for smart robots to learn semantic concepts from videos crawled to known Internet video/image websites (e.g. video-Baidu, Bing, Youku) is presented. An updated novel Internet video-mining method is addressed. An automatic graph model generator is addressed as well as the weight assignment for concepts-relationship learning based on known ontology and an automated video source discovery method in concepts detection from the massive Internet videos is proposed. Experimental results with Tera-bytes level videos show that the method is effective and efficient to solve the smart perception for intelligent robots.