人工智能
判别式
RGB颜色模型
计算机科学
模式识别(心理学)
计算机视觉
特征(语言学)
卷积神经网络
深度学习
水准点(测量)
作者
Zhitong Xiong,Yuan Yuan,Qi Wang
出处
期刊:IEEE transactions on image processing
[Institute of Electrical and Electronics Engineers]
日期:2021-01-27
卷期号:30: 2722-2733
被引量:6
标识
DOI:10.1109/tip.2021.3053459
摘要
Indoor scene images usually contain scattered objects and various scene layouts, which make RGB-D scene classification a challenging task. Existing methods still have limitations for classifying scene images with great spatial variability. Thus, how to extract local patch-level features effectively using only image labels is still an open problem for RGB-D scene recognition. In this paper, we propose an efficient framework for RGB-D scene recognition, which adaptively selects important local features to capture the great spatial variability of scene images. Specifically, we design a differentiable local feature selection (DLFS) module, which can extract the appropriate number of key local scenerelated features. Discriminative local theme-level and object-level representations can be selected with the DLFS module from the spatially-correlated multi-modal RGB-D features. We take advantage of the correlation between RGB and depth modalities to provide more cues for selecting local features. To ensure that discriminative local features are selected, the variational mutual information maximization loss is proposed. Additionally, the DLFS module can be easily extended to select local features of different scales. By concatenating the local-orderless and global structured multi-modal features, the proposed framework can achieve state-of-the-art performance on public RGB-D scene recognition datasets.
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