NDLI: Improved compound image segmentation using automatic pixel block classification with SVM

Content Provider	IET Digital Library
Author	Selwyn, Ebenezer Juliet Velayutham, Selvi Shunmuga George, Jemi Florinabel Deva
Abstract	Computer screen images such as wallpaper, web pages and powerpoint images are compound images. As these images contain a mixture of textual, graphical, pictorial and smooth regions, compression of computer screen images necessitates accurate classification and segmentation of these regions. In this study, an improved compound image segmentation using automatic block classification with support vector machine (SVM) is presented. First, the input compound image is divided into several non-overlapping blocks, and then the statistical features embedded in each block are mined after applying discrete wavelet transform. Then, the SVM model is trained effectively by using the informative samples of fuzzy c-means clustering, which takes block-level edge features and block neighbourhood information as input. Finally, the compound image is classified into two classes such as text/graphics and picture/background with the trained SVM model. Experimental results show that the proposed method performs automatic block classification with high accuracy. As the proposed classifier uses both structural and contextual information as features, block classification accuracy has been improved to a great extent. Hence, the proposed method has made ∼6.2% improvement in block classification accuracy while comparing with existing approaches.
Starting Page	1605
Ending Page	1613
Page Count	9
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 8, Jun (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/8
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.6523
Journal	IET Image Processing
Publisher Date	2019-11-27
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Automatic Pixel Block Classification Block Classification Accuracy Block Neighbourhood Information Block-level Edge Features Computer Screen Image Necessitates Accurate Classification Computer Vision And Image Processing Technique Discrete Wavelet Transform Edge Detection Feature Extraction Graphical Region Image And Video Coding Image Classification Image Coding Image Recognition Image Segmentation Improved Compound Image Segmentation Input Compound Image Method Performs Automatic Block Classification Nonoverlapping Blocks Optical, Image And Video Signal Processing Pattern Classification Pattern Clustering Pictorial Region Powerpoint Image Smooth Region Support Vector Machine Textual Region Trained SVM Model
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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