Tree-based Visualization and Optimization for Image Collection

TL;DR

This paper introduces a novel tree-based method for visualizing image collections in arbitrary shapes, allowing user-defined semantic correlations and adaptive layout changes, with effective optimization for layout quality.

Contribution

The paper presents a new property-based tree construction and a two-step optimization scheme for flexible, correlation-aware image collection visualization.

Findings

Effective control of layout shape and overlap ratio.

Adaptive layout changes based on user interest.

Outperforms state-of-the-art visualization methods.

Abstract

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.