A Survey of Developments and Challenges in Image Retargeting Using AI-Powered Seam Carving

Abstract

Image retargeting is a critical task in computer vision, aimed at resizing images while preserving their essential content and structure. Traditional methods like seam carving focus on removing low-energy pixels to achieve content-aware resizing, but often fall short in preserving important visual elements, especially in complex or dynamic images. Recent advancements in deep learning, particularly Convolutional Neural Networks (CNNs), have shown great promise in enhancing the effectiveness of seam carving by providing a more nuanced understanding of image content. This survey explores the integration of CNNs with the seam carving algorithm to develop an intelligent image retargeting system. By leveraging the hierarchical feature extraction capabilities of CNNs, the proposed system can better identify and preserve regions of interest during the resizing process. The review encompasses key methodologies in AI-driven image retargeting, including hybrid models combining deep learning and seam carving, as well as other AI-assisted retargeting techniques. The survey also highlights the challenges and gaps in current research, particularly regarding real-time processing, handling complex textures, and adapting to diverse image types. Finally, the survey discusses future directions for the integration of AI in image retargeting, emphasizing the need for further optimization to improve scalability, efficiency, and applicability across various platforms and devices.