Seam Carving Based Image Resizing Detection Using Hybrid Features

OpenAlex İngilizce

Original scientific paperDetection of seam carving-based digital image resizing is a challenging task in image processing field since the method investigates the images on hand semantically.Resizing with seam carving is realized by inserting or removing relatively unimportant pixel paths to/from the images and so the changes in image content are mostly unnoticeable.Local Binary Patterns (LBP), a visual descriptor, unearths local changes in image texture.Therefore, using LBP transform of the images besides intensity values contributes to the detection ratio.In this paper, we proposed a hybrid detection mechanism for more accurate seam carving detection especially in low scaling ratios.We extracted LBP-based and non-LBP based features and trained a Support Vector Machine (SVM) with sixty features.We achieved approximately 9 % improvement in low detection ratios.The experimental results show that more satisfactory detection ratios can be obtained by the proposed hybrid approach.