干旱区生态环境知识资源中心(Arid): Using Trained Pixel Classifiers to Select Images of Interest

Arid

来源ID	NTRS_Document_ID: 20040095337
	Using Trained Pixel Classifiers to Select Images of Interest
	Mazzoni, D.; Wagstaff, K.; Castano, R.
英文摘要	We present a machine-learning-based approach to ranking images based on learned priorities. Unlike previous methods for image evaluation, which typically assess the value of each image based on the presence of predetermined specific features, this method involves using two levels of machine-learning classifiers: one level is used to classify each pixel as belonging to one of a group of rather generic classes, and another level is used to rank the images based on these pixel classifications, given some example rankings from a scientist as a guide. Initial results indicate that the technique works well, producing new rankings that match the scientist's rankings significantly better than would be expected by chance. The method is demonstrated for a set of images collected by a Mars field-test rover.
英文关键词	MACHINE LEARNING IMAGE ANALYSIS PIXELS CLASSIFICATIONS ROVING VEHICLES RANKING PRIORITIES MARS SURFACE CLASSIFIERS MARS ROVING VEHICLES
出版年	2004
报告类型	Technical Report
语种	英语
国家	美国
URL	http://hdl.handle.net/2060/20040095337
资源类型	科技报告
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/259312
推荐引用方式 GB/T 7714	Mazzoni, D.,Wagstaff, K.,Castano, R.. Using Trained Pixel Classifiers to Select Images of Interest,2004.