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Convolutional fisher kernels for RGB-D object recognition
Cheng, Yanhua1; Cai, Rui2; Zhao, Xin1; Huang, Kaiqi1; Kaiqi Huang
2015
会议名称International Conference on 3D Vision
会议录名称Proc. International Conference on 3D Vision 2015
会议日期2015-10-01
会议地点France
摘要This paper studies the problem of improving object recognition using the novel RGB-D data. To address the problem, a new convolutional Fisher Kernels (CFK) method is proposed to represent RGB-D objects powerfully yet efficiently. The core idea of our approach is to integrate the both advantages of the convolutional neural networks (CNN) and Fisher Kernel encoding (FK): CNN model is flexible to adapt to new data sources, but requires for large amounts of training data with significant computational resources for good generalization, In comparison, FK encoding is able to represent objects powerfully and efficiently with small training data, however, its success highly depends on the well-designed SIFT features in literature, which may not be suitable for the new depth data. CFK can be interpreted as a two-layer feature learning structure to bridge the two models. The first layer employs a single-layer CNN to learn low-level translation ally invariant features for both RGB and depth data efficiently. The second layer aggregates the convolutional responses by FK encoding. Here 2D and 3D spatial pyramids are applied to further improve the Fisher vector representation of each modality. Experiments on RGB-D object recognition benchmarks demonstrate that our approach can achieve the state-of-the-art results.
关键词Rgb-d Recognition Fisher Kernel Cnn
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/12730
专题智能感知与计算研究中心
通讯作者Kaiqi Huang
作者单位1.中国科学院自动化研究所
2.微软亚洲研究院
推荐引用方式
GB/T 7714
Cheng, Yanhua,Cai, Rui,Zhao, Xin,et al. Convolutional fisher kernels for RGB-D object recognition[C],2015.
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