SBM14: Sketch Based Interfaces and Modeling 2014

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https://diglib.eg.org/handle/10.2312/15007

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Browsing SBM14: Sketch Based Interfaces and Modeling 2014 by Subject "Chemical Structure Sketch Prediction"

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    Mixed Heuristic Search for Sketch Prediction on Chemical Structure Drawing
    (ACM, 2014) Kang, Bo; Hu, Hao; LaViola, Joseph J.; Metin Sezgin
    Sketching is a natural way to input chemical structures that can be used to query information from a large chemical structure database. Based on a user's incomplete sketch of a chemical structure, sketch prediction becomes a challenging problem not only due to arbitrary drawings orders among users but also similarities among chemical structure layouts. In this paper, we present a graph-based approach to handle the sketch prediction problem. We use multisets as the data representation of hand-drawn chemical structures and create an undirected graph to handle data in all multisets. This approach transforms the sketch prediction problem into a search problem to find a hamiltonian path in the corresponding sub-graph with polynomial time complexity. We introduce mixed heuristics to guide the search procedure. Through an initial experiment on a hand-drawn chemical structure dataset, we demonstrate that in comparison with a baseline method, the proposed approach improves the prediction accuracy and efficiently predicts chemical structures from only partially sketched drawings.
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    Mixed Heuristic Search for Sketch Prediction on Chemical Structure Drawing
    (ACM, 2014) Kang, Bo; Hu, Hao; LaViola, Joseph J.; Metin Sezgin
    Sketching is a natural way to input chemical structures that can be used to query information from a large chemical structure database. Based on a user's incomplete sketch of a chemical structure, sketch prediction becomes a challenging problem not only due to arbitrary drawings orders among users but also similarities among chemical structure layouts. In this paper, we present a graph-based approach to handle the sketch prediction problem. We use multisets as the data representation of hand-drawn chemical structures and create an undirected graph to handle data in all multisets. This approach transforms the sketch prediction problem into a search problem to find a hamiltonian path in the corresponding sub-graph with polynomial time complexity. We introduce mixed heuristics to guide the search procedure. Through an initial experiment on a hand-drawn chemical structure dataset, we demonstrate that in comparison with a baseline method, the proposed approach improves the prediction accuracy and efficiently predicts chemical structures from only partially sketched drawings.