LOOP Descriptor: Local Optimal Oriented Pattern

TL;DR

The paper presents the LOOP descriptor, a rotation-invariant binary pattern that improves accuracy and efficiency in local feature encoding, demonstrated on lepidoptera species recognition and benchmark datasets.

Contribution

Introduction of the LOOP descriptor that inherently encodes rotation invariance, eliminating the need for post-processing and enhancing performance.

Findings

LOOP outperforms previous binary descriptors on multiple datasets.

LOOP achieves comparable or better accuracy with improved time complexity.

New NZ Lepidoptera dataset introduced for fine-grained species recognition.

Abstract

This letter introduces the LOOP binary descriptor (local optimal oriented pattern) that encodes rotation invariance into the main formulation itself. This makes any post processing stage for rotation invariance redundant and improves on both accuracy and time complexity. We consider fine-grained lepidoptera (moth/butterfly) species recognition as the representative problem since it involves repetition of localized patterns and textures that may be exploited for discrimination. We evaluate the performance of LOOP against its predecessors as well as few other popular descriptors. Besides experiments on standard benchmarks, we also introduce a new small image dataset on NZ Lepidoptera. Loop performs as well or better on all datasets evaluated compared to previous binary descriptors. The new dataset and demo code of the proposed method are to be made available through the lead author's academic webpage and GitHub.