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Seam Carving is a content-aware image resizing method capable of modifying the width or height to pictures preserving important elements. To evaluate the importance of each pixel this algorithm uses a energy function. The original algorithm can cause frequent object deformation in specific cases such as images containing people, because the energy function is not able to detect people. With this in mind, this paper presents a reformulation on the energy function of Seam Carving specific for preserving people in images. This energy function uses a neural network followed by morphological operations. For the experiments we used two dataset widely used for this type of problem RetargetMe and NRID. For comparison was used quality analysis metrics of resized images. The results show that the proposed method achieves superior performance in terms of visual quality via qualitative indices compared to the original algorithm. It also has outstanding results when compared to other resizing operators.

Neural network used in Energy Function of Content-aware Image Resizing Method to Preserve Skin Color Region