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- Title
Improved compound image segmentation using automatic pixel block classification with SVM.
- Authors
Juliet Selwyn, Ebenezer; 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.
- Publication
IET Image Processing (Wiley-Blackwell), 2020, Vol 14, Issue 8, p1605
- ISSN
1751-9659
- Publication type
Article
- DOI
10.1049/iet-ipr.2018.6523