In this paper, we propose a novel approach for performing joint recomposition and retargeting of photographic images (R2P). Given a reference image of interest, our method is able to automatically alter the composition of the input source image accordingly, while the recomposed output will be jointly retargeted to fit the reference. This is achieved by recomposing the visual components of the source image via graph matching, followed by solving a constrained mesh-warping based optimization problem for retargeting. As a result, the recomposed output image would fit the reference while suppressing possible distortion. Our experiments confirm that our proposed R2P method is able to achieve visually satisfactory results, without the need to use pre-collected labeled data or predetermined aesthetics rules.

In this paper, we propose novel photography recomposition method, which aims at transferring the photography composition of a reference image to an input image automatically. Without any user interaction, our approach first identifies the salient foreground objects or image regions of interest, and the recomposition is performed by solving a graph-matching based optimization task. With additional post-processing step to preserve the locality and boundary information of the recomposed visual components, we can solve the task of photography recomposition without the uses of any prior knowledge on photography or predetermined image aesthetics rules. Experiments on a variety of images, including transferring the photography composition from real photos, sketches or even paintings, would confirm the effectiveness of our proposed method.

This paper presents a saliency-based video object extraction (VOE) framework. The proposed framework aims to automatically extract foreground objects of interest without any user interaction or the use of any training data (i.e., not limited to any particular type of object). To separate foreground and background regions within and across video frames, the proposed method utilizes visual and motion saliency information extracted from the input video. A conditional random field is applied to effectively combine the saliency induced features, which allows us to deal with unknown pose and scale variations of the foreground object (and its articulated parts). Based on the ability to preserve both spatial continuity and temporal consistency in the proposed VOE framework, experiments on a variety of videos verify that our method is able to produce quantitatively and qualitatively satisfactory VOE results.

In this paper, we propose a saliency inspired video object extraction (VOE) method to extract and segment foreground objects of interest from videos captured by freely moving cameras. Our method aims at detecting visual and motion salient regions from an input video, and thus we integrate such cosaliency information with the associated foreground and background color models to achieve VOE. A conditional random field (CRF) is applied in our framework to automatically identify the foreground object regions based on the above features, while our method does not need any prior knowledge on the foreground objects of interest or any interaction from the users. Experiments on a variety of videos confirm that our method is able to provide quantitatively and qualitatively more satisfactory results when comparing to state-of-the-art VOE approaches.