Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/88461
PIRA download icon_1.1View/Download Full Text
Title: Gaussian mixture model and Gaussian supervector for image classification
Authors: Jiang, Y 
Leung, HF 
Issue Date: Nov-2018
Source: 2018 IEEE 23rd International Conference on Digital Signal Processing (DSP), Shanghai, China, China, 19-21 Nov. 2018, p. 1-5
Abstract: Gaussian Mixture Model (GMM) has been widely used in speech signal and image signal classification tasks. It can be directly used as a classifier, or used as the representation of speech or image signals. Another important usage of GMM is to serve as the Universal Background Model (UBM) to generate speech representations such as Gaussian Supervector (GSV) and i-vector. In this paper, we borrow GSV from speech signal classification studies and apply it as an image representation for image classification. GSV is calculated based on a Universal Background Model (UBM). Apart from employing the conventional GMM as the UBM to calculate GSV, we also propose the Equal-Variance GMM (EV-GMM), where all the variables in all the Gaussian mixture components share the same variance. Moreover, we derive the kernel version of EV-GMM, which generalizes EV-GMM by introducing a kernel. We then compare GSV to the raw image feature and other popular image representations such as Sparse Representation (SR) and Collaborative Representation (CR). Experiments are carried out on a handwritten digit recognition task, and classification results indicate that GSV can work very well and can be even better than other popular image representations. In addition, as the UBM, the proposed EV-GMM can work better than the conventional GMM.
Keywords: Gaussian mixture model
Equal-variance Gaussian mixture model
Gaussian supervector
Image classification
Publisher: Institute of Electrical and Electronics Engineers
DOI: 10.1109/ICDSP.2018.8631558
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
The following publication Y. Jiang and H. F. Frank Leung, "Gaussian Mixture Model and Gaussian Supervector for Image Classification," 2018 IEEE 23rd International Conference on Digital Signal Processing (DSP), Shanghai, China, 2018, pp. 1-5 is available at https://dx.doi.org/10.1109/ICDSP.2018.8631558
Appears in Collections:Conference Paper

Files in This Item:
File Description SizeFormat 
Jiang_Gaussian_Mixture_Model.pdfPre-Published version970.58 kBAdobe PDFView/Open
Open Access Information
Status open access
File Version Final Accepted Manuscript
Access
View full-text via PolyU eLinks SFX Query
Show full item record

Page views

79
Last Week
0
Last month
Citations as of Mar 24, 2024

Downloads

14
Citations as of Mar 24, 2024

SCOPUSTM   
Citations

5
Citations as of Mar 29, 2024

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.