Efficient bat-inspired block matching algorithm with novel motion energy directional histograms for dynamic texture fast recognition

Motion estimation is a crucial step in Dynamic Texture (DT) Motion-based recognition systems, as it directly affects the system's overall accuracy and computational proficiency. Whereas efficient motion estimation methods are pixel-based prioritizing recognition quality over computational complexity, this trade-off may not be acceptable in time-sensitive applications. Therefore, Block Matching algorithms (BMA) stand as potential alternative candidates, however, classic BMA often falls into local optimums ambushes resulting suboptimal solutions, particularly for complex motion videos such DT. In this paper, we propose a novel bat-inspired block matching approach for motion estimation to overcome the issue of falling into local optima. Our approach is inspired from the powerful search capacity of the bat algorithm seeking the best block within a search space; aiming accuracy improvement and a faster convergence. Afterwards, we extract motion features from the matched blocks fields to characterize the different DT videos. Two comprehensive sets of experiments were performed to validate the proposed approaches, Firstly the bat-inspired block motion estimation performance was compared against common algorithms on various standard video sequences. Moreover, the effectiveness of the introduced Dynamic Texture Recognition (DTR) system was demonstrated using well-known DT datasets, where comparative studies with state-of-art methods were presented. The results achieved a balance on computational speed and accuracy on multiple datasets.

Automated summary

In this paper, a novel bat-inspired block matching approach for motion estimation is proposed to overcome the issue of falling into local optima. Motion features are extracted from the matched blocks fields to characterize different dynamic texture videos. Experimental results demonstrate the effectiveness of the introduced Dynamic Texture Recognition (DTR) system in terms of computational speed and accuracy.