Recurrence Quantification Analysis Statistics for Image Feature Extraction and Classification

Advances in computer vision technology have expanded the possibilities to facilitate complex task automation for integration into large-scale data processing solutions. Despite these advances, however, there is still a need to develop simple and efficient algorithms for image feature extraction and classification to enable easier and faster implementation into real-world applications. Here, a new method is described to extract features from images that can be used for image classification. It uses a fuzzy c-means (FCM) clustering-based approach that allows for unique object patterns to be spatially re-mapped onto a binary sparse matrix with which principles from recurrence quantification analysis statistics (RQAS) can be applied. RQAS are computationally efficient and can be used to create a short feature vector for effective binary and multi-class image classification. The utility of this method is demonstrated using both simulated and real datasets that include objects embedded in complex backgrounds, and is compared with another widely used and highly effective thresholding feature extraction method (local binary patterns (LBP)). Results show that the FCM-RQAS method described here can perform as well or better than LBP and supports the use and further development of RQAS-based image feature extraction for computer vision applications.