State-of-the-art approaches, we here exploit subjective experiments to modelĪnd learn pleasantness from user preferences. The task isįormulated as an optimization problem. In this paper we consider how to automatically create pleasing photo collagesĬreated by placing a set of images on a limited canvas area. Finally, we also propose a tree-transfer scheme such that visualization layouts can be adaptively changed when users select different "images of interest." We demonstrate the effectiveness of our proposed approach through the comparisons with state-of-the-art visualization techniques. As a result, multiple layout effects including layout shape and image overlap ratio can be effectively controlled to guarantee a satisfactory visualization. Then, we design a two-step visualization optimization scheme to further optimize image layouts. In this way, images can be adaptively placed with the desired semantic or visual correlations in the final visualization layout. To this end, we first propose a property-based tree construction scheme to organize images of a collection into a tree structure according to user-defined properties. This paper focuses on an important problem that is not well addressed by the previous methods: visualizing image collections into arbitrary layout shapes while arranging images according to user-defined semantic or visual correlations (e.g., color or object category). The visualization of an image collection is the process of displaying a collection of images on a screen under some specific layout requirements. To illustrate the power of AutoCollage, we have used it to create collages of many home photo sets we also conducted a user study in which AutoCollage outperformed competitive methods. Rather than attempt an expensive, integrated optimization procedure, we have developed a sequence of optimization steps, from static ranking of images, through region of interest optimization, optimal packing by constraint satisfaction, and lastly graph-cut alpha-expansion. Secondly the resulting optimization poses a search problem that, on the face of it, is computationally in-feasible. Firstly, we show how energy terms can be included that: encourage the selection of a representative set of images that are sensitive to particular object classes that encourage a spatially efficient and seamless layout. This paper makes several new contributions. It is also assembled largely seamlessly, using graph-cut, Poisson blending of alpha-masks, to hide the joins between input images. The aim is that the resulting collage should be representative of the collection, summarising its main themes. The paper defines an automatic procedure for constructing a visually appealing collage from a collection of input images.
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February 2023
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