Query Adaptive Similarity Measure for RGB-D Object Recognition | |
Yanhua Cheng; Rui Cai; Chi Zhang; Zhiwei Li; Xin Zhao; Kaiqi Huang; Yong Rui | |
2015 | |
会议名称 | International Conference on Computer Vision |
会议录名称 | Proc. International Conference on Computer Vision 2015 |
页码 | 145-153 |
会议日期 | 2015-12-01 |
会议地点 | Santiago, Chile |
摘要 | This paper studies the problem of improving the top-1 accuracy of RGB-D object recognition. Despite of the impressive top-5 accuracies achieved by existing methods, their top-1 accuracies are not very satisfactory. The reasons are in two-fold: (1) existing similarity measures are sensitive to object pose and scale changes, as well as intra-class variations, and (2) effectively fusing RGB and depth cues is still an open problem. To address these problems, this paper first proposes a new similarity measure based on dense matching, through which objects in comparison are warped and aligned, to better tolerate variations. Towards RGB and depth fusion, we argue that a constant and golden weight doesn't exist. The two modalities have varying contributions when comparing objects from different categories. To capture such a dynamic characteristic, a group of matchers equipped with various fusion weights is constructed, to explore the responses of dense matching under different fusion configurations. All the response scores are finally merged following a learning-to-combination way, which provides quite good generalization ability in practice. The proposed approach win the best results on several public benchmarks, e.g., achieves 92.7% top-1 test accuracy on the Washington RGB-D object dataset, with a 5.1% improvement over the state-of-the-art. |
关键词 | Art cameras feature Extraction image Color Analysis market Research object Recognition shape |
语种 | 英语 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/12680 |
专题 | 智能感知与计算研究中心 |
通讯作者 | Kaiqi Huang |
作者单位 | 中国科学院自动化研究所 |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Yanhua Cheng,Rui Cai,Chi Zhang,et al. Query Adaptive Similarity Measure for RGB-D Object Recognition[C],2015:145-153. |
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