Octagonal lattice-based triangulated shape descriptor engaging second-order derivatives supplementing image retrieval.

• Engagement of octagonal lattice-based triangulated feature characterization, which is the first of its kind. • The inherent congruency of the geometrical arrangement makes the descriptor robust to numerous image transformations. • The nature of the second-order derivatives capture keenly high-frequency information such as edges, corners, and points. • Thorough investigations on benchmark shape datasets demonstrate the contribution's superiority. • Complexity analysis reveals the simplicity of the OLTT model signifying its compatibility with real scenarios. Erstwhile shape description schemes lack primarily in establishing trade-offs with accuracy and computational load. Accordingly, a lightweight shape descriptor offering precise definition and compaction of high-frequency features is contributed in this paper using a simple geometrical shape for localization and shape characterization. Initially, the input image is octagonally tessellated and triangularly decomposed into sub-regions whose side-wise differences are evaluated and subjected to second-order differentiation to produce three high-frequency values representing triangle corners. The resultant is processed by the law of sines to yield localized shape features exhibiting congruence and is reiterated on the residual regions, followed by a novel octal encoding scheme encompassing maximal variations in the localized regions. The resulting features are globally fabricated into shape histograms in a non-overlapping manner representing the shape vector. This scheme validated on widely popular benchmark shape datasets demonstrates superior retrieval and recognition accuracies greater than 93% which is lacking in its competitors. [ABSTRACT FROM AUTHOR]