Large Scale Image Collection Visualization
Large Image Collection Visualization Using Perception-Based Similarity with Color Features
Zeyuan Chen and Christopher G. Healey,
in International Symposium on Visual Computing, pp. 379–390. Springer, 2016. [paper]
This paper introduces the basic steps to build a similarity-based visualization tool for large image collections. We build the similarity metrics based on human perception. Psychophysical experiments have shown that human observers can recognize the gist of scenes within 100 milliseconds (msec) by comprehending the global properties of an image. Color also plays an important role in human rapid scene recognition. However, previous works often neglect color features. We propose new scene descriptors that preserve the information from coherent color regions, as well as the spatial layouts of scenes. Experiments show that our descriptors outperform existing state-of-the-art approaches. Given the similarity metrics, a hierarchical structure of an image collection can be built in a top-down manner. Representative images are chosen for image clusters and visualized using a force-directed graph.
Planar Object Recognition and Pose Estimation
Recognize planar images and estimate their pose with respect to the ground.
We combine the estimated pose and the gravity sensor of iPhone to compute the planar object’s pose with respect to the ground.