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Query Adaptive Similarity Measure for RGB-D Object Recognition
Yanhua Cheng; Rui Cai; Chi Zhang; Zhiwei Li; Xin Zhao; Kaiqi Huang; Yong Rui
Conference NameInternational Conference on Computer Vision
Source PublicationProc. International Conference on Computer Vision 2015
Conference Date2015-12-01
Conference PlaceSantiago, Chile
AbstractThis 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.
KeywordArt   cameras   feature Extraction   image Color Analysis   market Research   object Recognition   shape
Document Type会议论文
Corresponding AuthorKaiqi Huang
Recommended Citation
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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