Automated mark localization in scatter images, greatly helpful for
discovering knowledge and understanding enormous document images and reasoning
in visual question answering AI systems, is a highly challenging problem
because of the ubiquity of overlapping marks. Locating overlapping marks faces
many difficulties such as no texture, less contextual information, hallow shape
and tiny size. Here, we formulate it as a combinatorial optimization problem on
clustering-based re-visualization, to locate scatter marks by finding the
status of multi-variables when an objective function reaches a minimum. The
objective function is constructed on difference between binarized scatter
images and corresponding re-visualization based on their clustering.
Fundamentally, re-visualization tries to redraw a new scatter graph only taking
a rasterized scatter image as an input, and clustering is employed to provide
the information for such re-visualization. This method could stably locate
severely-overlapping, variable-size and variable-shape marks in scatter images
without dependence of any training dataset or reference. Meanwhile, we propose
an adaptive variant of simulated annealing which can works on various connected
regions. In addition, we especially built a dataset named SML2023 containing
hundreds of scatter images with different markers and various levels of
overlapping severity, and tested the proposed method and compared it to
existing methods. The results show that it can accurately locate most marks in
scatter images with different overlapping severity and marker types, with about
0.3 absolute increase on an assignment-cost-based metric in comparison with
state-of-the-art methods. This work is of value to data mining on massive web
pages and literatures, and shedding new light on image measurement such as
bubble counting.

通过基于聚类的重新可视化，解决了在散点图中定位重叠标记时的困难，并提出了一种适应不同连通区域的自适应模拟退火方法，通过实验验证了该方法在定位散点图中标记方面的准确性和优越性。