Sparse Distance Learning for Object Recognition Combining RGB and Depth Information
In this work we address joint object category and instance recognition in the context of RGB-D (depth) cameras. Motivated by local distance learning, where a novel view of an object is compared to individual views of previously seen objects, we define a view-to-object distance where a novel view is compared simultaneously to all views of a previous object. This novel distance is based on a weighted combination of feature differences between views. We show, through jointly learning perview weights, that this measure leads to superior classification performance on object category and instance recognition. More importantly, the proposed distance allows us to find a sparse solution via Group-Lasso regularization, where a small subset of representative views of an object is identified and used, with the rest discarded. This significantly reduces computational cost without compromising recognition accuracy. We evaluate the proposed technique, Instance Distance Learning (IDL), on the RGB-D Object Dataset, which consists of 300 object instances in 51 everyday categories and about 250,000 views of objects with both RGB color and depth.We empirically compare IDL to several alternative state-of-the-art approaches and also validate the use of visual and shape cues and their combination.
Kevin Lai Liefeng Bo Xiaofeng Ren Dieter Fox
Department of Computer Science & Engineering,University of Washington,Seattle,WA 98195,USA Intel Labs Seattle,Seattle,WA 98105,USA Department of Computer Science & Engineering,University of Washington,and Intel Labs Seattle
国际会议
2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)
上海
英文
4007-4013
2011-05-09（万方平台首次上网日期，不代表论文的发表时间）