Detecting seam carved images using uniform local binary patterns
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Seam carving is widely used excellent content-aware image scaling method. When an image is processed by seam carving, its local texture changes. Local binary patterns is an excellent local descriptor for describing the local texture of an image. In this paper, a blind detection based uniform local binary patterns(ULBP) is proposed to detect seam-carved image. Firstly, the image is transformed into gray-scale image. Then the ULBP histogram features and seam features are extracted from the gray-scale image. Finally support vector machine (SVM) is adopted as classifier to train and test those features to identify whether an image is subjected to seam carving or not. Wei et al. (Pattern Recogn Lett 36:100–106 2014) method and Ryu et al. (IEICE Trans Inf Syst 97(5):1304–1311 2014) method are selected as the benchmark. Extensive compared experiments are conducted by the three methods, respectively. Experimental results show that the proposed method has the best performance among the three methods under a variety of setting.
KeywordsSeam carving Images retargeting Uniform local binary patterns (ULBP) Support vector machine
This work is supported in part by the National Key R&D Program of China (2018YFB1003205), the National Natural Science Foundation of China (61772087,61572183, 61379143, 61232016, U1405254), the Specialized Research Fund for the Doctoral Program of Higher Education (SRFDP) under grant 20120161110014, the Scientific Research Fund of Hunan Provincial Education Department of China (14C0029). Prof. Arun Kumar Sangaiah is the corresponding author.
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