Image-based dinosaur classification presents unique challenges due to limited data, large intra-class shape variability, and strong pose and illustration-style differences. Conventional integer-order image operators often fail to capture both fine-scale texture and global morphological structure simultaneously, particularly in small-sample data science challenges. This work explores fractional-order (FO) image processing and pattern recognition as an alternative representation framework for dinosaur image analysis.
We propose a fractional-order computer vision pipeline combining three complementary components. First, fractional-order gradient and edge operators are applied to dinosaur images to capture multi-scale contour and texture information associated with skeletal structure, armour, horns, wings, and body outlines. Second, fractional spectral descriptors, derived from fractional Fourier and multi-resolution wavelet statistics, model long-range spatial correlations and anisotropic shape patterns. Third, fractional-order pattern recognition tools, including fractional diffusion embeddings and fractional-order feature statistics, are used for exploratory data analysis, visualization, and classification. The approach is evaluated on two datasets: (i) a five-class dinosaur dataset with limited samples per class, and (ii) a 40-class carnivorous–herbivorous dinosaur dataset with train/validation splits. Fractional features are coupled with classical machine-learning classifiers, avoiding data-hungry deep models.
Experimental analysis shows that fractional-order representations provide improved class separability as compared to integer-order baselines, particularly for visually similar dinosaur species. Fractional diffusion embeddings reveal smooth morphological manifolds that align with anatomical traits such as body mass, posture, and feeding type. In the carnivorous–herbivorous dinosaur classification task, FO features demonstrate robust discrimination despite large inter-species variation and small sample sizes.
This study demonstrates that fractional-order image processing and pattern recognition offer an effective, interpretable, and data-efficient framework for dinosaur image analysis. By introducing a continuous order parameter, fractional methods naturally capture multi-scale shape and texture variations, making them well suited for paleontological and educational vision tasks with limited image data.
