Alwaely, B. orcid.org/0000-0002-1845-2641 and Abhayaratne, C. orcid.org/0000-0002-2799-7395 (2023) GHOSM : Graph-based Hybrid Outline and Skeleton Modelling for shape recognition. ACM Transactions on Multimedia Computing, Communications, and Applications, 19 (2s). 86. pp. 1-23. ISSN 1551-6857
Abstract
An efficient and accurate shape detection model plays a major role in many research areas.With the emergence of more complex shapes in real life applications, shape recognition models need to capture the structure with more effective features in order to achieve high accuracy rates for shape recognition. This paper presents a new method for 2D/3D shape recognition based on graph spectral domain handcrafted features, which are formulated by exploiting both an outline and a skeleton shape through the global outline and internal details. A fully connected graph is generated over the shape outline to capture the global outline representation while a hierarchically clustered graph with adaptive connectivity is formed on the skeleton to capture the structural descriptions of the shape. We demonstrate the ability of the Fiedler vector to provide the graph partitioning of the skeleton graph. The performance evaluation demonstrates the efficiency of the proposed method compared to state-of-the-art studies with increments of 4.09%, 2.2% and 14.02% for 2D static hand gestures, 2D shapes and 3D shapes, respectively.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2022 Association for Computing Machinery. This is an author-produced version of a paper subsequently published in ACM Transactions on Multimedia Computing, Communications and Applications. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Graph Matching; Spectral Graph Partitioning; Static Hand Gesture |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 08 Sep 2022 12:37 |
Last Modified: | 24 Feb 2023 16:07 |
Status: | Published |
Publisher: | Association for Computing Machinery (ACM) |
Refereed: | Yes |
Identification Number: | 10.1145/3554922 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:190833 |