Learning discriminative representations from RGB-D video data

Liu, Li and Shao, Ling (2013) Learning discriminative representations from RGB-D video data. In: IJCAI-13 - 23rd International Joint Conference on Artificial Intelligence, 3rd - 9th August 2013, Beijing, China.

Full text not available from this repository. (Request a copy)

Abstract

Recently, the low-cost Microsoft Kinect sensor, which can capture real-time high-resolution RGB and depth visual information, has attracted increasing attentions for a wide range of applications in computer vision. Existing techniques extract hand-tuned features from the RGB and the depth data separately and heuristically fuse them, which would not fully exploit the complementarity of both data sources. In this paper, we introduce an adaptive learning methodology to automatically extract (holistic) spatio-temporal features, simultaneously fusing the RGB and depth information, from RGB-D video data for visual recognition tasks. We address this as an optimization problem using our proposed restricted graph-based genetic programming (RGGP) approach, in which a group of primitive 3D operators are first randomly assembled as graph-based combinations and then evolved generation by generation by evaluating on a set of RGB-D video samples. Finally the best-performed combination is selected as the (near-)optimal representation for a pre-defined task.

The proposed method is systematically evaluated on a new hand gesture dataset, SKIG, that we collected ourselves and the public MSR Daily Activity 3D dataset, respectively. Extensive experimental results show that our approach leads to significant advantages compared with state-of-the-art hand-crafted and machine-learned features.

Item Type: Conference or Workshop Item (Paper)
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Paul Burns
Date Deposited: 16 Jun 2015 12:30
Last Modified: 10 Aug 2015 11:08
URI: http://nrl.northumbria.ac.uk/id/eprint/22948

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics


Policies: NRL Policies | NRL University Deposit Policy | NRL Deposit Licence