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Automated mark localization in scatter images, greatly helpful fordiscovering knowledge and understanding enormous document images and reasoningin visual question answering AI systems, is a highly challenging problembecause of the ubiquity of overlapping marks. Locating overlapping marks facesmany difficulties such as no texture, less contextual information, hallow shapeand tiny size. Here, we formulate it as a combinatorial optimization problem onclustering-based re-visualization from a non-training generative perspective,to locate scatter marks by finding the status of multi-variables when anobjective function reaches a minimum. The objective function is constructed ondifference between binarized scatter images and corresponding generatedre-visualization based on their clustering. Fundamentally, re-visualizationtries to generate a new scatter graph only taking a rasterized scatter image asan input, and clustering is employed to provide the information for suchre-visualization. This method could stably locate severely-overlapping,variable-size and variable-shape marks in scatter images without dependence ofany training dataset or reference. Meanwhile, we propose an adaptive variant ofsimulated annealing which can works on various connected regions. In addition,we especially built a dataset named SML2023 containing hundreds of scatterimages with different markers and various levels of overlapping severity, andtested the proposed method and compared it to existing methods. The resultsshow that it can accurately locate most marks in scatter images with differentoverlapping severity and marker types, with about 0.3 absolute increase on anassignment-cost-based metric in comparison with state-of-the-art methods. Thiswork is of value to data mining on massive web pages and literatures, andshedding new light on image measurement such as bubble counting.

OsmLocator: locating overlapping scatter marks with a non-training generative perspective