Hierarchical String Cuts: A Translation, Rotation, Scale and Mirror Invariant Descriptor for Fast Shape Retrieval
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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.
IEEE Transactions on Image Processing
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Pattern Recognition and Data Mining