Tree-based Visualization and Optimization for Image Collection
This addresses the need for flexible and user-customizable image visualization tools, though it is incremental as it builds on existing methods.
The paper tackles the problem of visualizing image collections into arbitrary layout shapes while arranging images based on user-defined semantic or visual correlations, achieving effective control over layout effects like shape and overlap ratio through comparisons with state-of-the-art techniques.
The visualization of an image collection is the process of displaying a collection of images on a screen under some specific layout requirements. 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). 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. In this way, images can be adaptively placed with the desired semantic or visual correlations in the final visualization layout. Then, we design a two-step visualization optimization scheme to further optimize image layouts. As a result, multiple layout effects including layout shape and image overlap ratio can be effectively controlled to guarantee a satisfactory visualization. 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.