IIUM Repository (IREP)

Viewpoint invariant semantic object and scene categorization with RGB-D sensors

Mohd Zaki, Hasan Firdaus and Shafait, Faisal and Mian, Ajmal (2019) Viewpoint invariant semantic object and scene categorization with RGB-D sensors. Autonomous Robots, 43 (4). pp. 1005-1022. ISSN 0929-5593 (In Press)

[img] PDF (Evidence from publishers' website for MYRA) - Published Version
Restricted to Repository staff only

Download (3MB) | Request a copy
[img]
Preview
PDF (Scopus) - Supplemental Material
Download (176kB) | Preview
[img]
Preview
PDF (WOS)
Download (112kB) | Preview

Abstract

Understanding the semantics of objects and scenes using multi-modal RGB-D sensors serves many robotics applications. Key challenges for accurate RGB-D image recognition are the scarcity of training data, variations due to viewpoint changes and the heterogeneous nature of the data. We address these problems and propose a generic deep learning framework based on a pre-trained convolutional neural network, as a feature extractor for both the colour and depth channels. We propose a rich multi-scale feature representation, referred to as convolutional hypercube pyramid (HP-CNN), that is able to encode discriminative information from the convolutional tensors at different levels of detail. We also present a technique to fuse the proposed HP-CNN with the activations of fully connected neurons based on an extreme learning machine classifier in a late fusion scheme which leads to a highly discriminative and compact representation. To further improve performance, we devise HP-CNN-T which is a view-invariant descriptor extracted from a multi-view 3D object pose (M3DOP) model. M3DOP is learned from over 140,000 RGB-D images that are synthetically generated by rendering CAD models from different viewpoints. Extensive evaluations on four RGB-D object and scene recognition datasets demonstrate that our HP-CNN and HP-CNN-T consistently outperforms state-of-the-art methods for several recognition tasks by a significant margin.

Item Type: Article (Journal)
Additional Information: 8293/64696
Uncontrolled Keywords: Object categorization; Scene recognition; RGB-D image; Multi-modal deep learning
Subjects: Q Science > Q Science (General) > Q300 Cybernetics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Kulliyyahs/Centres/Divisions/Institutes: Kulliyyah of Engineering > Department of Mechatronics Engineering
Depositing User: Dr. Hasan Firdaus Mohd Zaki
Date Deposited: 24 Jul 2018 12:17
Last Modified: 01 Aug 2019 10:25
URI: http://irep.iium.edu.my/id/eprint/64696

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year