Query Adaptive Similarity Measure for RGB-D Object Recognition
文献类型：会议论文
| 作者 | Yanhua Cheng ; Rui Cai; Chi Zhang; Zhiwei Li; Xin Zhao; Kaiqi Huang; Yong Rui
|
| 出版日期 | 2015 |
| 会议日期 | 2015-12-01 |
| 会议地点 | Santiago, Chile |
| 关键词 | Art cameras feature Extraction image Color Analysis market Research object Recognition shape |
| 页码 | 145-153 |
| 英文摘要 | 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. |
| 会议录 | Proc. International Conference on Computer Vision 2015
 |
| 语种 | 英语 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/12680]  |
| 专题 | 自动化研究所_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 |
| 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]. 见:. Santiago, Chile. 2015-12-01. |
入库方式： OAI收割
来源：自动化研究所
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