Automatic blur type classification via ensemble SVM

• Published in: Signal processing. Image communication Vol. 71; pp. 24 - 35
• Main Authors: Wang, Rui, Li, Wei, Li, Rui, Zhang, Liang
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.02.2019; Elsevier BV
• Subjects: Basis functions; Blur image classification; Classifiers; Communication; Computer simulation; Digital imaging; Domains; Ensemble SVM classifier; Feature ranking; Feature selection; Flicker; Haze; Image classification; Image restoration; Optimization; Radial basis function; Random sampling; Recursive methods; Signal processing; State of the art; Support vector machine-recursive feature elimination (SVM-RFE); Support vector machines; Support vector rate (SVR); Websites; Ensemble SVM classifier; Support vector machine-recursive feature elimination (SVM-RFE); Feature selection; Support vector rate (SVR); Feature ranking; Blur image classification
• ISSN: 0923-5965; 1879-2677
• DOI: 10.1016/j.image.2018.08.003

Automatic classification of blur type is critical to blind image restoration. In this paper, we propose an original solution for blur type classification of digital images using ensemble Support Vector Machine (SVM) structure. It is assumed that each image is subject to at most one of three blur types: haze, motion, and defocus In the proposed technique, 35 blur features are first calculated from image spatial and transform domains, and then ranked using the SVM-Recursive Feature Elimination (SVM-RFE) method, which is also adopted to optimize the parameters of the Radial Basis Function (RBF) kernel of SVMs. Moreover, Support Vector Rate (SVR) is used to quantify the optimal number of features to be included in the classifiers. Finally, the bagging random sampling method is utilized to construct the ensemble SVM classifier based on a weighted voting mechanism to classify the types of blurred images. Numerical experiments are conducted over a sample dataset to be called Beihang Univ. Blur Image Database (BHBID) that consists of 1188 simulated blurred images and 1202 natural blurred images collected from popular national and international websites (Baidu.com, Flicker.com, Pabse.com, etc.). The experiments demonstrate the superior performance of the proposed ensemble SVM classifier by comparing it with single SVM classifiers as well as other state-of-the-art blur classification methods. •An ensemble SVM classifier is first applied to blur image classification.•SVR is adopted to rank the significance of the extracted blur features.•The haze blur images is considered for the first time in blur image classification.