Abstract:
In this study, new feature descriptors are designed for medical image retrieval and change detection applications,
respectively. Inspired by isomerism, the authors propose a novel feature descriptor named antithetic isomeric cluster pattern
(ANTIC). The ANTIC is defined by the two properties: cluster patterns and antithetic isomerism (ANTI). The cluster pattern
corresponds to successive pixel intensity differences at antithetical orientations. Furthermore, the ANTI is characterised by two
aspects: first, the clusters are oppositely oriented (antithetical) to each other and second, both adhere to a defined isomeric
property. The ANTIC identifies the lines and corner point information in the local neighbourhood across various directions. To
attain enhanced robustness, they further proposed multiresolution ANTIC by integrating the multiresolution Gaussian filter.
Moreover, to reduce the feature dimensionality, they extended their work to rotation invariant features. The proposed method
outperforms other widely used feature descriptors in biomedical and retinopathy image retrieval applications. In addition, they
extracted spatiotemporal features by designing intra-ANTIC and inter-ANTIC to detect motion changes in video sequences.
They validated the effectiveness of these features by conducting experiments on CDNet 2014 dataset. The proposed descriptor
achieves better performance in various challenging conditions for change detection as compared to other state-of-the-art
techniques.
