Structure-aware multimodal feature fusion for RGB-D scene classification and beyond

While convolutional neural networks (CNNs) have been excellent for object recognition, the greater spatial variability in scene images typically means that the standard full-image CNN features are suboptimal for scene classification. In this article, we investigate a framework allowing greater spati...

Full description

Saved in:
Bibliographic Details
Main Authors: Wang, Anran, Cai, Jianfei, Lu, Jiwen, Cham, Tat-Jen
Other Authors: School of Computer Science and Engineering
Format: Article
Language:English
Published: 2020
Subjects:
Online Access:https://hdl.handle.net/10356/138263
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
Description
Summary:While convolutional neural networks (CNNs) have been excellent for object recognition, the greater spatial variability in scene images typically means that the standard full-image CNN features are suboptimal for scene classification. In this article, we investigate a framework allowing greater spatial flexibility, in which the Fisher vector (FV)-encoded distribution of local CNN features, obtained from a multitude of region proposals per image, is considered instead. The CNN features are computed from an augmented pixel-wise representation consisting of multiple modalities of RGB, HHA, and surface normals, as extracted from RGB-D data. More significantly, we make two postulates: (1) component sparsity—that only a small variety of region proposals and their corresponding FV GMM components contribute to scene discriminability, and (2) modal nonsparsity—that features from all modalities are encouraged to coexist. In our proposed feature fusion framework, these are implemented through regularization terms that apply group lasso to GMM components and exclusive group lasso across modalities. By learning and combining regressors for both proposal-based FV features and global CNN features, we are able to achieve state-of-the-art scene classification performance on the SUNRGBD Dataset and NYU Depth Dataset V2. Moreover, we further apply our feature fusion framework on an action recognition task to demonstrate that our framework can be generalized for other multimodal well-structured features. In particular, for action recognition, we enforce interpart sparsity to choose more discriminative body parts, and intermodal nonsparsity to make informative features from both appearance and motion modalities coexist. Experimental results on the JHMDB and MPII Cooking Datasets show that our feature fusion is also very effective for action recognition, achieving very competitive performance compared with the state of the art.