On the other hand, the rapid development of mobile technology also calls for a robust solution of fast collage generation without any computation-expensive processes such as saliency detection and graph-cut. However, automatic saliency detection can sometimes be harmful since we cannot guarantee all of the user's interest areas are well-kept.
Previous approaches for automatic collage generation are always analogized as optimization problems, in which the researchers are trying to find the best balance between maximizing the visibility of photos' salient areas as well as compactly arrange the collage canvas layout. Photo collage, which constructs a compact and visually appealing representation from a collection of input images, provides the best convenient and impressive user experience. The effectiveness of social attribute annotation is proved as well. Experiments show the examples of Friend Wall. To effectively arrange the photos on a single canvas, we proposed a binary tree based representation and fast algorithm for layout generation. In our definition, social attributes contain a set of intrinsic labels such as Who, When, Where, and What.
Motivated by the observation that SNS (Social Networking Service) images, especially those come from the user’s acquaintances always contain rich information, we apply these images as our training set and explore their social attributes. For the first problem, we propose a novel image annotation scheme by employing both of the image visual features and Metadata.
Picasa collage maker how to#
(2) How to efficiently generate layout to compactly arrange many photos onto a single canvas. The so-called Friend Wall system solves two problems: (1) How to effectively classify the local images with respect to related social characters and events. In this paper, we focus on providing a novel image browsing and visualization experience for local photo repository. We also present several extensions and applications oriented to a variety of usage contexts and device platforms. The proposed algorithm is fast, requiring less than 0.5 ms to generate a 100-image collage.
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Based on a full balanced binary layout tree, our algorithm can pack all the input images tightly onto the collage canvas while keeping their visual information unchanged. In our alternative approach, we address the issue of content-preserved collage, which avoids content-harmful processes such as cropping or changes to aspect ratio and orientation. Even if the main regions of interest are retained accurately, some visually not salient but semantically important items such as logos, captions, and copyright information located at the margins and corners may be missed. Moreover, the effectiveness of automatic saliency detection may be questionable. However, such methods are computationally expensive and not feasible for real-time applications such as online image retrieval or interactive photo browsing. Most previous approaches to collage construction have utilized saliency detection and visibility optimization. Photo collage, which constructs a compact and visually appealing representation from a collection of input images, can offer a most convenient and impressive user experience. 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.
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