Summarization-Based Image Resizing by Intelligent Object Carving

Abstract

Abstract-Image resizing can be more effectively achieved with a better understanding of image semantics. In this paper, similar patterns that exist in many real-world images are analyzed. By interactively detecting similar objects in an image, the image content can be summarized rather than simply distorted or cropped. This method enables the manipulation of image pixels or patches as well as semantic objects in the scene during image resizing process. Given the special nature of similar objects in a general image, the integration of a novel object carving (OC) operator with the multi-operator framework is proposed for summarizing similar objects. The object removal sequence in the summarization strategy directly affects resizing quality. The method by which to evaluate the visual importance of the object as well as to optimally select the candidates for object carving is demonstrated. To achieve practical resizing applications for general images, a template matching-based method is developed. This method can detect similar objects even when they are of various colors, transformed in terms of perspective, or partially occluded. To validate the proposed method, comparisons with state-of-the-art resizing techniques and a user study were conducted. Convincing visual results are shown to demonstrate the effectiveness of the proposed method.

A Survey on Content Aware Image Resizing Methods

“…These are 4 basic principles of human visual attention. For saliency detection in the image, a multitude of methods which are explained in [19,20,21,22] can be used. In image and video compression the outcome of the saliency detection algorithm can be used [23].…”

“…They also proposed a framework based on a multi-operator to integrate users' real visual preferences tightly. Dong et al [19] suggested a resizing method based on summarization. In this method, object carving is performed based on a multi-operator framework.…”