A local-global shape characterization scheme using quadratic Bezier triangle aiding retrieval.

Shape characterization plays a highly prominent role in retrieval and relies extremely upon descriptors inbuilt with lightweight operations and compaction qualities. However, realizing such a simple and robust shape descriptor capable of dealing with noise, variations in brightness, and deformations pose a significant challenge. In this regard, a simple and effective shape descriptor using the Quadratic Bezier Triangle (QBT) targeting shape matching and retrieval is presented in this work. The mechanism commences with triangular tessellation of each image followed by determining their side-wise intensity differences that are then mapped to QBT vertices to yield the order-wise control points. The maxima of the resulting points are transformed into the binary equivalent of the given shape that is compacted into octal values. Later, these localized values are globally transformed into shape histograms to yield the QBT-based Feature (QBTF) descriptor representing the given input. QBTFs performance is exhaustively analyzed in terms of Bulls Eye Retrieval (BER) score and classification accuracy using the public datasets namely Kimia-99, MPEG-7 CE-1 part B, PHOS, and Tari-1000. Relative BER investigations witnessed across diverse datasets reveal a consistent and improved score of 93% achieved by this scheme over its peers. Mathematical analysis of invariance, noise resilience, and the complexities involved in realizing QBTF, establish the robustness and suitability of this scheme for real-time shape description. • Quadratic Bezier Triangle for shape characterization is the first of its kind and is the core novelty of this contribution. • The engaged Bernstein polynomials acutely capitulate high-frequency features deemed essential for shape characterization. • Inherent convex nature of QBT demonstrates high robustness towards diverse affine transformations, noise, and illumination. • Rigorous analysis performed on benchmark shape datasets reveals the intention's superiority. • The proposed shape descriptor's performance matches with black-box models at a relatively lesser computational cost. [ABSTRACT FROM AUTHOR]