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  • Hierarchical String Cuts: A Translation, Rotation, Scale and Mirror Invariant Descriptor for Fast Shape Retrieval

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    Author(s)
    Wang, Bin
    Gao, Yongsheng
    Griffith University Author(s)
    Gao, Yongsheng
    Wang, Bin
    Year published
    2014
    Metadata
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    Abstract
    This paper presents a novel approach for both fast and accurately retrieving similar shapes. A hierarchical string cuts (HSCs) method is proposed to partition a shape into multiple level curve segments of different lengths from a point moving around the contour to describe the shape gradually and completely from the global information to the finest details. At each hierarchical level, the curve segments are cut by strings to extract features that characterize the geometric and distribution properties in that particular level of details. The translation, rotation, scale, and mirror invariant HSC descriptor enables a fast ...
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    This paper presents a novel approach for both fast and accurately retrieving similar shapes. A hierarchical string cuts (HSCs) method is proposed to partition a shape into multiple level curve segments of different lengths from a point moving around the contour to describe the shape gradually and completely from the global information to the finest details. At each hierarchical level, the curve segments are cut by strings to extract features that characterize the geometric and distribution properties in that particular level of details. The translation, rotation, scale, and mirror invariant HSC descriptor enables a fast metric-based matching to achieve the desired high accuracy. Encouraging experimental results on four databases demonstrated that the proposed method can consistently achieve higher (or similar) retrieval accuracies than the state-of-the-art benchmarks with a more than 120 times faster speed. This may suggest a new way of developing shape retrieval techniques in which a high accuracy can be achieved by a fast metric matching algorithm without using the time-consuming correspondence optimization strategy.
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    Journal Title
    IEEE Transactions on Image Processing
    Volume
    23
    Issue
    9
    DOI
    https://doi.org/10.1109/TIP.2014.2343457
    Copyright Statement
    © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subject
    Computer vision
    Cognitive and computational psychology
    Publication URI
    http://hdl.handle.net/10072/67250
    Collection
    • Journal articles

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